AI Accelerator
Land an AI engineering role in as little as 90 days, without going back to school, grinding through YouTube tutorials, or needing any prior AI experience.
We build your personalized roadmap, help you build a production-grade portfolio, apply for jobs on your behalf, and prep you for interviews, all the way through to a signed offer. If you don't land a role within 6 months of us starting to apply on your behalf, you get 100% of your tuition back.
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Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
Zao Yang
Owner of \newline and previously co-creator of Farmville (200M users, $3B revenue) and Kaspa ($3B market cap). Self-taught in gaming, crypto, deep learning, now generative AI. Newline is used by 250,000+ professionals from Salesforce, Adobe, Disney, Amazon, and more. Newline has built editorial tools using LLMs, article generation using reinforcement learning and LLMs, instructor outreach tools. Newline is currently building generative AI products that will be announced soon.
01Personalized Roadmap
We start with a skillset audit to map out exactly where you are versus where you need to be. Based on your current stack, experience, and target roles, we build you a roadmap that skips what you already know and focuses only on what gets you hired.
02Production-Grade Portfolio
You don't just learn concepts, you build real systems with senior technical mentorship reviewing your work at every step. By the end, you have a portfolio that proves you can ship AI products, not just talk about them.
03Done-For-You Job Search
Once your portfolio is ready, we take over the job search. We optimize your LinkedIn, build your resume, apply to roles on your behalf, and prep you for every interview round. You wake up to interview requests instead of spending your nights filling out forms.
04Offer Negotiation
When offers come in, we help you negotiate the full package, including base, bonus, equity, and signing bonus. We've seen engineers add tens of thousands to their offers with a 20-minute negotiation strategy most people don't know.
Understand the lifecycle of large language models, from training to inference
Build and deploy a fully functional LLM Inference API
Master tokenization techniques, including byte-pair encoding and word embeddings
Develop foundational models like n-grams and transition to transformer-based models
Implement self-attention and feed-forward neural networks in transformers
Evaluate LLM performance using metrics like perplexity
Deploy models using modern tools like Huggingface, Modal, and TorchScript
Adapt pre-trained LLMs through fine-tuning and retrieval-augmented generation (RAG)
Leverage state-of-the-art tools for data curation and adding ethical guardrails
Apply instruction-tuning techniques with low-rank adapters
Explore multi-modal LLMs integrating text, voice, images, and robotics
Understand machine learning operations, from project scoping to deployment
Design intelligent agents with planning, reflection, and collaboration capabilities
Keep up-to-date with AI trends, tools, and industry best practices
Receive technical reviews and mentorship to refine your projects
Create a robust portfolio showcasing real-world AI applications
What This Program Actually Does
We've stripped out everything that doesn't directly move you toward a signed AI engineering offer.
No padded curriculum, no theory you'll never use, no projects that look good in a classroom but mean nothing to a hiring manager.
Instead, we focus on three things:
We make the AI landscape make sense. You'll walk away understanding how modern AI systems actually work, from the models that power ChatGPT to the retrieval systems companies are deploying right now. Not at a trivia level. At a level where you can sit in a room with an AI team and hold your own from day one.
We build your portfolio with you. You don't figure out what to build or how to build it. We give you the exact projects that hiring managers are looking for, and senior technical mentors review every line of code. What you ship during the program is production-grade work, the kind that makes hiring managers think "this person can actually do the job" instead of "this person watched some videos."
We get you hired. This is the part most programs skip. Once your portfolio is ready, we apply for jobs on your behalf, optimize your LinkedIn, prep you for every interview round, and help you negotiate your offer. We stay with you until you have a signed offer in hand.
Over the course of the program, you'll build everything from classical language models to full transformer architectures from scratch. You'll work with the same tools being used in production at leading AI labs, and you'll see the decisions that separate systems that actually ship from systems that stall out in development.
But the technical content is only half of what we do. The other half is getting you in front of the right hiring managers with a portfolio, resume, and positioning that makes you the obvious choice.
Because understanding AI doesn't get you hired. Being able to prove you can build AI systems and getting in front of the people who care does.
Who You'll Be Learning From
Dr. Dipen Bhuva has a PhD focused on LLMs in cybersecurity, 150+ research citations, and 16 published papers including tier-1 publications in Elsevier and IEEE Access. He's collaborated with NASA Glenn Research Center, the Cleveland Clinic, and the U.S. Department of Energy.
What that means for you: the technical backbone of this program has been built by someone who spends his time inside the research itself. Dipen reads nearly every newly-published AI paper and distills what's actually useful into frameworks you can apply. You're not guessing what matters in a field that moves this fast, he's already figured it out for you.
Zao Yang has been building software products for 15+ years. He co-created FarmVille (200 million users, $3 billion in revenue), founded Kaspa ($3B market cap), and runs Newline, an education platform used by over 250,000 engineers from companies like Salesforce, Adobe, Disney, and Amazon. He's invested in over 130 startups across gaming, crypto, and AI.
What that means for you: Zao is actively building AI products right now, which means he knows what tools teams are actually using in production, what hiring managers are looking for in interviews, and what separates engineers who get offers from engineers who get ghosted. The curriculum is informed by real market signals, not theory.
A signed AI engineering offer. That's the primary outcome. Everything else supports this.
A personalized roadmap that skips the unnecessary theory. Built around your current stack and target roles, so every hour you invest moves you closer to the role, not in random directions.
A production-grade portfolio. Real systems built with senior mentorship, not toy projects. The kind that makes hiring managers pay attention.
A positioned LinkedIn and AI-focused resume. Written for you, not a template. Ready to put in front of recruiters the moment your portfolio is done.
A done-for-you job search. We apply on your behalf, track responses, and build a pipeline of interviews so you're not spending your nights filling out forms.
Interview and negotiation prep. Mock interviews with our team, coaching for behavioral rounds, and a negotiation strategy that can add tens of thousands to your offer.
A 6-month guarantee. Follow the system, and if you don't land a role within 6 months of us starting to apply on your behalf, you get 100% of your tuition back.
Our students work at
Bootcamp Syllabus and Content
Onboarding & Tooling
3 Units
- 01AI Onboarding & Python Essentials
-
Welcome & Community
- Course Overview
- Community: Getting Started with Circle and Notion
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Python & Tooling Essentials
- Intro to Python: Why Python for AI and Why Use Python 3 (Not Python 2)
- Install Python and Set Virtual Environments
- Basic Python Introduction: Variables, Data Types, Loops & Functions
- Using Jupyter Notebooks
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Introduction to AI Tools & Ecosystem
- Introduction to AI & Why Learn It
- Models & Their Features: Choosing the Right AI Model for Your Needs
- Finding and Using AI Models & Datasets: A Practical Guide with Hugging Face
- Using Restricted vs Unrestricted Open-Source AI Models from Hugging Face
- Hardware for AI: GPUs, TPUs, and Apple Silicon
- Advanced AI Concepts Worth Knowing
- Practical Tips on How to Be Productive with AI
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Brainstorming Ideas with AI
- Brainstorming with Prompting Tools
-
- 02Orientation — Course Introduction
- Meet the instructors and understand the support ecosystem (Circle, Notion, async help)
- Learn the 4 learning pillars: concept clarity, muscle memory, project building, and peer community
- Understand course philosophy: minimize math, maximize intuition, focus on real-world relevance
- Set up accountability systems, learning tools, and productivity habits for long-term success
- 03Orientation — Technical Kickoff
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Jupyter & Python Setup
- Understanding why Python is used in AI (simplicity, libraries, end-to-end stack)
- Exploring Jupyter Notebooks: shortcuts, code + text blocks, and cloud tools like Google Colab
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Hands-On with Arrays, Vectors, and Tensors
- Creating and manipulating 2D and 3D NumPy arrays (reshaping, indexing, slicing)
- Performing matrix operations: element-wise math and dot products
- Visualizing vectors and tensors in 2D and 3D space using matplotlib
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Mathematical Foundations in Practice
- Exponentiation and logarithms: visual intuition and matrix operations
- Normalization techniques and why they matter in ML workflows
- Activation functions: sigmoid and softmax with coding from scratch
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Statistics and Real Data Practice
- Exploring core stats: mean, standard deviation, normal distributions
- Working with real datasets (Titanic) using Pandas: filtering, grouping, feature engineering, visualization
- Preprocessing tabular data for ML: encoding, scaling, train/test split
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Bonus Topics
- Intro to probability, distributions, classification vs regression
- Tensor intuition and compute providers (GPU, Colab, cloud vs local)
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AI Projects and Use Cases
3 Units
- 01Navigating the Landscape of LLM Projects & Modalities
- Compare transformer-based LLMs vs diffusion models and their use cases
- Understand the "lego blocks" of LLM-based systems: prompts, embeddings, generation, inference
- Explore core LLM application types: RAG, vertical models, agents, and multimodal apps
- Learn how LLMs are being used in different roles and industries (e.g., healthcare, finance, legal)
- Discuss practical project scoping: what to build vs outsource, how to identify viable ideas
- Identify limitations of LLMs: hallucinations, lack of reasoning, sensitivity to prompts
- Highlight real-world startup examples (e.g., AutoShorts, HeadshotPro) and venture-backed tools
- 02From Theory to Practice — Building Your First LLM Application
- Understand how inference works in LLMs (prompt processing vs. autoregressive decoding)
- Explore real-world AI applications: RAG, vertical models, agents, multimodal tools
- Learn the five phases of the model lifecycle: pretraining to RLHF to evaluation
- Compare architecture types: generic LLMs vs. ChatGPT vs. domain-specialized models
- Work with tools like Hugging Face, Modal, and vector databases
- Build a “Hello World” LLM inference API using OPT-125m on Modal
- 03Intro to AI-Centric Evaluation
- Metrics and Evaluation Design
- Foundation for Future Metrics Work
- Building synthetic data for AI applications
Prompt Engineering & Embeddings
2 Units
- 01Prompt Engineering — From Structure to Evaluation (Mini Project 1)
- Learn foundational prompt styles: vague vs. specific, structured formatting, XML-tagging
- Practice prompt design for controlled output: enforcing strict JSON formats with Pydantic
- Discover failure modes and label incorrect LLM behavior (e.g., hallucinations, format issues)
- Build early evaluators to measure LLM output quality and rule-following
- Write your first "LLM-as-a-judge" prompts to automate pass/fail decisions
- Iterate prompts based on analysis-feedback loops and evaluator results
- Explore advanced prompting techniques: multi-turn, rubric-based human alignment, and A/B testing
- Experiment with
dspyfor signature-based structured prompting and validation
- 02Tokens, Embeddings & Modalities — Foundations of Understanding Text, Image, and Audio
- Understand the journey from raw text → tokens → token IDs → embeddings
- Compare word-based, BPE, and advanced tokenizers (LLaMA, GPT-2, T5)
- Analyze how good/bad tokenization affects loss, inference time, and semantic meaning
- Learn how embedding vectors represent meaning and change with context
- Explore and manipulate Word2Vec-style word embeddings through vector math and dot product similarity
- Apply tokenization and embedding logic to multimodal models (CLIP, ViLT, ViT-GPT2)
- Conduct retrieval and classification tasks using image and audio embeddings (CLIP, Wav2Vec2)
- Discuss emerging architectures like Byte Latent Transformers and their implications
Multimodal + Retrieval-Augmented Systems
2 Units
- 01Multimodal Embeddings (CLIP)
- Understand how CLIP learns joint image-text representations using contrastive learning
- Run your first CLIP similarity queries and interpret shared embedding space
- Practice prompt engineering with images — and see how wording shifts retrieval results
- Build retrieval systems: text-to-image and image-to-image using cosine similarity
- Experiment with visual vector arithmetic: apply analogies to embeddings
- Explore advanced tasks like visual question answering (VQA) and image captioning
- Compare multimodal architectures: CLIP, ViLT, ViT-GPT2 and how they process fusion
- Learn how modality-specific encoders (image/audio) integrate into transformer models
- 02RAG & Retrieval Techniques (Mini Project 2)
- Understand the full RAG pipeline: pre-retrieval, retrieval, and post-retrieval stages
- Learn the difference between term-based and embedding-based retrieval methods (e.g., TF-IDF, BM25 vs. vector search)
- Explore vector databases, chunking, and query optimization techniques like HyDE, reranking, and filtering
- Use contrastive learning and cosine similarity to map queries and documents into shared vector spaces
- Practice retrieval evaluation using
recall@k,precision@k, andMRR - Generate synthetic data using LLMs (Instructor, Pydantic) for local eval scenarios
- Implement baseline vector search pipelines using LanceDB and OpenAI embeddings (3-small, 3-large)
- Apply rerankers and statistically validate results with bootstrapping and t-tests to build intuition around eval reliability
Classical Language Models
2 Units
- 01N-Gram Language Models (Mini Project 3)
- Understand what n-grams are and how they model language with simple probabilities
- Implement bigram and trigram extraction using sliding windows over character sequences
- Construct frequency dictionaries and normalize into probability matrices
- Sample random text using bigram and trigram models to generate synthetic sequences
- Evaluate model quality using entropy, character diversity, and negative log likelihood (NLL)
- One-hot encode inputs and build PyTorch models for bigram and trigram neural networks
- Train models with cross-entropy loss and monitor training dynamics
- Compare classical vs. neural models in terms of coherence, prediction accuracy, and generalization
- 02Triplet Loss Embedding Finetuning for Search & Ranking (Mini Project 4)
- Triplet-Based Embedding Adaptation
- User-to-Music & E-commerce Use Cases
Attention & Finetuning
2 Units
- 01Building Self-Attention Layers
- Understand the motivation for attention: limitations of fixed-window n-gram models
- Explore how word meaning changes with context using static vs contextual embeddings (e.g., "bank" problem)
- Learn the mechanics of self-attention: Query, Key, Value, dot products, and weighted sums
- Manually compute attention scores and visualize how softmax creates probabilistic context focus
- Implement self-attention layers in PyTorch using toy examples and evaluate outputs
- Visualize attention heatmaps using real LLMs to interpret which words the model attends to
- Compare loss curves of self-attention models vs trigram models and observe learning dynamics - Understand how embeddings evolve through transformer layers and extract them using GPT-2
- Build both single-head and multi-head transformer models; compare their predictions and training performance
- Implement a Mixture-of-Experts (MoE) attention model and observe gating behavior on different inputs
- Evaluate self-attention vs MoE vs n-gram models on fluency, generalization, and loss curves
- Run meta-evaluation across all models to compare generation quality and training stability
- 02Instructional Finetuning with LoRA (Mini Project 5)
- Understand the difference between fine-tuning and instruction fine-tuning (IFT)
- Learn when to apply fine-tuning vs IFT vs RAG based on domain, style, or output needs
- Explore lightweight tuning methods like LoRA, BitFit, and prompt tuning
- Build instruction-tuned systems for outputs like JSON, tone, formatting, or domain tasks
- Apply fine-tuning to real case studies: HTML generation, resume scoring, financial tasks
- Use Hugging Face PEFT tools to train and evaluate LoRA-tuned models
- Understand tokenizer compatibility, loss choices, and runtime hardware considerations
- Compare instruction-following performance of base vs IFT models with real examples
Architectures & Multimodal Systems
2 Units
- 01Feedforward Networks & Loss-Centric Training
- Understand the role of linear + nonlinear layers in neural networks
- Explore how MLPs refine outputs after self-attention in transformers
- Learn the structure of FFNs (e.g., two-layer projection + activation like ReLU/SwiGLU)
- Implement your own FFN in PyTorch with real training/evaluation
- Compare activation functions: ReLU, GELU, SwiGLU
- Understand how dropout prevents co-adaptation and improves generalization
- Learn the role of LayerNorm, positional encoding, and skip connections
- Build intuition for how transformers encode depth, context, and structure into layers
- 02Multimodal Finetuning (Mini Project 6)
- Understand what CLIP is and how contrastive learning aligns image/text modalities
- Fine-tune CLIP for classification (e.g., pizza types) or regression (e.g., solar prediction)
- Add heads on top of CLIP embeddings for specific downstream tasks
- Compare zero-shot performance vs fine-tuned model accuracy
- Apply domain-specific LoRA tuning to vision/text encoders
- Explore regression/classification heads, cosine similarity scoring, and decision layers
- Learn how diffusion models extend CLIP-like embeddings for text-to-image and video generation
- Understand how video generation differs via temporal modeling, spatiotemporal coherence
Assembling & Training Transformers
2 Units
- 01Full Transformer Architecture (From Scratch)
- Connect all core transformer components: embeddings, attention, feedforward, normalization
- Implement skip connections and positional encodings manually
- Use sanity checks and test loss to debug your model assembly
- Observe transformer behavior on structured prompts and simple sequences
- Compare transformer predictions vs earlier trigram or FFN models to appreciate context depth
- 02Advanced RAG & Retrieval Methods
- Analyze case studies on production-grade RAG systems and tools like Relari and Evidently
- Understand common RAG bottlenecks and solutions: chunking, reranking, retriever+generator coordination
- Compare embedding models (small vs large) and reranking strategies
- Evaluate real-world RAG outputs using recall, MRR, and qualitative techniques
- Learn how RAG design changes based on use case (enterprise Q&A, citation engines, document summaries)
Specialized Finetuning Projects
2 Units
- 01CLIP Fine-Tuning for Insurance
- Fine-tune CLIP to classify car damage using real-world image categories
- Use Google Custom Search API to generate labeled datasets from scratch
- Apply PEFT techniques like LoRA to vision models and optimize hyperparameters with Optuna
- Evaluate accuracy using cosine similarity over natural language prompts (e.g. “a car with large damage”)
- Deploy the model in a real-world insurance agent workflow using LLaMA for reasoning over predictions
- 02Math Reasoning & Tool-Augmented Finetuning
- Use SymPy to introduce symbolic reasoning to LLMs for math-focused applications
- Fine-tune with Chain-of-Thought (CoT) data that blends natural language with executable Python
- Learn two-stage finetuning: CoT → CoT+Tool integration
- Evaluate reasoning accuracy using symbolic checks, semantic validation, and regression metrics
- Train quantized models with LoRA and save for deployment with minimal resource overhead
Advanced RLHF & Engineering Architectures
2 Units
- 01Preference-Based Finetuning — DPO, PPO, RLHF & GRPO
- Learn why base LLMs are misaligned and how preference data corrects this
- Understand the difference between DPO, PPO, RLHF, and GRPO
- Generate math-focused DPO datasets using numeric correctness as preference signal
- Apply ensemble voting to simulate “majority correctness” and eliminate hallucinations
- Evaluate model learning using preference alignment instead of reward models
- Compare training pipelines: DPO vs RLHF vs PPO — cost, control, complexity
- 02Building AI Code Agents — Case Studies from Copilot, Cursor, Windsurf
- Reverse engineer modern code agents like Copilot, Cursor, Windsurf, and Augment Code
- Compare transformer context windows vs RAG + AST-powered systems
- Learn how indexing, retrieval, caching, and incremental compilation create agentic coding experiences
- Explore architecture of knowledge graphs, graph-based embeddings, and execution-aware completions
- Design your own multi-agent AI IDE stack: chunking, AST parsing, RAG + LLM collaboration
Agents & Multimodal Code Systems
2 Units
- 01Agent Design Patterns
- Understand agent design patterns: Tool use, Planning, Reflection, Collaboration
- Learn evaluation challenges in agent systems: output variability, partial correctness
- Study architecture patterns: single-agent vs constellation/multi-agent
- Explore memory models, tool integration, and production constraints
- Compare agent toolkits: AutoGen, LangGraph, CrewAI, and practical use cases
- 02Text-to-SQL and Text-to-Music Architectures
- Implement text-to-SQL using structured prompts and fine-tuned models
- Train and evaluate SQL generation accuracy using execution-based metrics
- Explore text-to-music pipelines: prompt → MIDI → audio generation
- Compare contrastive vs generative learning in multimodal alignment
- Study evaluation tradeoffs for logic-heavy vs creative outputs
Deep Internals & Production Pipelines
2 Units
- 01Positional Encoding + DeepSeek Internals
- Understand why self-attention requires positional encoding
- Compare encoding types: sinusoidal, RoPE, learned, binary, integer
- Study skip connections and layer norms: stability and convergence
- Learn from DeepSeek-V3 architecture: MLA (KV compression), MoE (expert gating), MTP (parallel decoding), FP8 training
- Explore when and why to use advanced transformer optimizations
- 02LLM Production Chain (Inference, Deployment, CI/CD)
- Map the end-to-end LLM production chain: data, serving, latency, monitoring
- Explore multi-tenant LLM APIs, vector databases, caching, rate limiting
- Understand tradeoffs between hosting vs using APIs, and inference tuning
- Plan a scalable serving stack (e.g., LLM + vector DB + API + orchestrator)
- Learn about LLMOps roles, workflows, and production-level tooling
Enterprise LLMs, Hallucinations & Career Growth
4 Units
- 01RAG Hallucination Control & Enterprise Search
- Explore use of RAG in enterprise settings with citation engines
- Compare hallucination reduction strategies: constrained decoding, retrieval, DPO
- Evaluate model trustworthiness for sensitive applications
- Learn from production examples in legal, compliance, and finance contexts
- 02Career Prep — Roles, Interviews, and AI Career Paths
- Break down roles: AI Engineer, Model Engineer, Researcher, PM, Architect
- Prepare for FAANG/LLM interviews with DSA, behavioral prep, and project portfolio
- Use ChatGPT and other tools for mock interviews and story crafting
- Learn how to build a standout AI resume, repo, and demo strategy
- Explore internal AI projects, indie hacker startup paths, and transition guides
- 03Staying Current with AI (Research, News, and Tools)
- Track foundational trends: RAG, Agents, Fine-tuning, RLHF, Infra
- Understand tradeoffs of long context windows vs retrieval pipelines
- Compare agent frameworks (CrewAI vs LangGraph vs Relevance AI)
- Learn from real 2025 GenAI use cases: productivity + emotion-first design
- Stay current via curated newsletters, YouTube breakdowns, and community tools
- 04Bonus Content
- 2 courses - Fundamentals of transformers with Alvin Wan and Responsive LLM Applications with Server-Sent Events
- Prompt engineering templates
- AI newsletters, channels, X, reddit channels
- Break down of LLama components
- Open source models with their capabilities
- Data sources
- AI specific cloud services
- Open source frameworks
- Project ideas from other indie hackers
- Bonus: FANG Machine learning interview cheatsheet
- Free API Keys for building AI Applications
- How people will are using GenAI in 2025
- How to stay ahead of AI Trends?
- N8N and Free High-Roi AI Automation Templates worth $50,000
AI Accelerator
1 Unit
- 01AI Accelerator
-
Foundations & AI Applications
- Pick a profitable AI niche and validate real demand using structured research
- Learn AI product templates that connect user problems to proven UX flows and AI stacks
- Build audience-first distribution strategies across social, content, communities, and funnels
-
Statistics, Evaluations & Synthetic Data
- Set up Python tooling, Jupyter notebooks, and virtual environments
- Build core AI intuition with vectors, tensors, matrices, NumPy, and probability
- Understand transformer LLM applications across text, code, images, audio, and video
- Build your first LLM inference API and learn evaluation-based AI engineering
- Generate synthetic QA datasets and apply LLM-as-Judge workflows for calibration
-
Prompt Engineering, Embeddings & RAG
- Master zero-shot, few-shot, chain-of-thought, and defensive prompt design
- Learn tokenization, dense embeddings, and multimodal alignment with CLIP
- Build RAG pipelines with chunking, vector databases, semantic retrieval, and reranking
- Explore n-gram language models and connect them to neural network foundations
- Survey all forms of fine-tuning: domain, instructional, LoRA, RLHF, DPO, and GRPO
-
Transformers & Fine-Tuning
- Implement self-attention, cross-attention, and modern inference optimizations
- Fine-tune embeddings with triplet loss, contrastive tuning, and hard-negative mining
- Rebuild GPT-2 (124M) from scratch in PyTorch with advanced training mechanics
- Fine-tune multimodal encoders for classification, regression, and domain-specific tasks
-
Pre-Training, Post-Training & Agents
- Build and train full decoder-only transformer stacks with DDP and gradient accumulation
- Design agent architectures with perception, planning, tool-use, and observation loops
- Study Mixture-of-Experts routing, modern LLM architectures, and reasoning emergence
- Implement advanced RAG with multi-hop reasoning, Graph RAG, and agentic retrieval
- Apply DPO, RLHF, PPO, and GRPO for preference-based model alignment
- Build AI code agents inspired by Copilot, Cursor, and Windsurf architectures
-
Case Studies & Production AI
- Build real-time text-to-voice systems with sub-second latency and streaming pipelines
- Design production-grade AI with guardrails, structured outputs, and scaling patterns
- Explore text-to-video generation with Diffusion Transformers, Sora, and Wan 2.2
- Create browser agents with vision-to-code workflows and DOM-based reasoning
- Learn how to stay current with AI trends, frameworks, and emerging techniques
-
Hands-On Projects & Community
- Complete 50+ code exercises and 4 competition-based mini projects
- Build and demo a personal or professional AI project
- Attend weekly live lectures, Q&A sessions, and group coaching calls
- Join an in-person mastermind event in Miami for collaboration and networking
-
Career Prep
1 Unit
- 01Career Prep
-
Job Guaranteed Program
- Complete all program requirements and pass internal evaluations to unlock full job placement support
- We apply to roles on your behalf, send direct outreach to recruiters and hiring managers, and manage follow-ups
-
Program Requirements
- Complete all 8 approved mini projects on GitHub with full documentation and demo videos
- Make at least one accepted open-source contribution
- Finalize and get approval on your resume, LinkedIn, and portfolio
-
Internal Interviews
- Pass a technical AI engineering interview
- Pass a coding and system design interview
- Pass a leadership, communication, and hiring-manager interview
-
Job Placement & Guarantee
- Direct recruiter and hiring manager outreach with your projects, GitHub, and portfolio
- Interview guarantee and job offer guarantee once all requirements are met
- Every introduction positions you as a strong, credible AI engineer
-
Career Coaching & Support
- GitHub review, resume reviews, and career coaching sessions
- Mock interview practices with AI-powered critique loops
- AI engineering interview question bank
- Personalized career path guidance across AI Engineer, Model Engineer, and Research Engineer roles
-
Resources
You’ll receive a comprehensive set of resources to help you master large language models.
Prompt engineering templates
AI newsletters, channels, X, reddit channels
Break down of LLama components
Break down of Mistral components
Bonus
Unlock exclusive bonuses to accelerate your AI journey.
Be able to build large language models, which can increase your salaries by $50k a year. Worth $500k over 10 years.
Cheatsheet on generative AI interviews for FANGs, a $50k a year over a $500k value.
A complete course on end to end streaming Langchain with a fully functional application for startups. $15k in value.
Be able run consulting in AI $100k in annual value. Over 10 years. $1m.
Be able to build an AI company $1M in annual value.
Technical and business design review from Alvin and Zao about your project. $25000 dollars in value.
Become our next success story
The market is hungry for AI engineers, and our system is built to turn you into one. Book your free AI Career Strategy Session and let's map out exactly what your path to an AI engineering role looks like.
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Book a call with usFrequently Asked Questions
How is this different from other AI programs?
Most AI programs are designed to teach you AI. Ours is designed to get you hired.
That's the core difference, and it changes everything downstream.
Traditional programs, whether university degrees or AI bootcamps, run everyone through the same curriculum and hand you a certificate at the end. What you do after that is your problem. Go apply to jobs, figure out LinkedIn, hope your portfolio is good enough, compete with every other grad who has the same projects you do.
We built ours around a single outcome: you with a signed AI engineering offer. That means everything else, including the curriculum, the mentorship, the job search, and the interview prep, is built in service of that outcome. We don't hand you a certificate and disappear. We stay with you until you're hired, backed by a 100% money-back guarantee if you follow the system and don't land a role within 6 months of us starting to apply on your behalf.
What should I look for in an AI program?
If you're evaluating AI programs, here's what actually matters:
Is the curriculum based on what's being used in production right now, or what worked 18 months ago? AI moves fast. If the content is more than a few months old, it's already behind.
Does the program help you get hired, or just teach you? Most programs stop at "we taught you AI, good luck." If that's the bar, you're paying for content you could find for free.
Is there a real guarantee? Not "we guarantee you'll complete a project." A real guarantee tied to the actual outcome you're paying for, which is a role.
Who's building the program, and are they still in the field? If the instructors aren't actively working in AI, their knowledge is going stale fast.
We built our program around all four of these, which is why we can offer a 100% money-back guarantee on outcomes.
What we'd recommend doing is to just copy the curriculum and give it to some LLMs with the context of you being an experienced SWE who wants to transition into AI engineering - ask it to compare it with other curricula, what companies want, what works in prod, etc., and you'll get your answer.
Who is this program for?
This program is built for software engineers who want to move into AI engineering roles.
That includes backend engineers, full-stack engineers, frontend engineers, mobile engineers, DevOps, and pretty much any flavor of engineer who's been watching the AI wave and wondering how to get in.
There are three tracks, depending on what you're after:
The AI Engineer track is the one most of our students take. We map out your personalized roadmap, help you build a production-grade portfolio, apply to roles on your behalf, and prep you for interviews until you have a signed offer in hand.
The AI Engineer with Research Paper track is for engineers targeting the top end of the market, the $300k to $500k+ roles at leading AI labs and research-focused teams. These roles require a different bar, so we help you co-author and publish a research paper that positions you for that level. This track typically takes 3 to 6 months longer than our main program, and the curriculum goes deeper into the theory that these kinds of roles require, which is why it's not the right fit for most people. But if you're aiming for the top tier and willing to put in the extra time, this is the path.
The Internal Promotion track uses the same curriculum as the AI Engineer track, but applied inside your current company. We help you identify an AI project at your company, build it with senior mentorship, and leverage it into a promotion. About 30% of our students take this path, and many get their company to pay for the program.
We'll help you figure out which track makes the most sense for your situation on your strategy session.
What are the eligibility criteria?
You need to be able to program. That's the main prerequisite.
Basic Python helps, but if you're coming from another language, you can pick up what you need quickly. We'll guide you through any gaps.
You don't need prior AI or ML experience. You don't need a PhD or a math background. Some of our most successful students came in with zero AI knowledge and landed roles at companies like L'Oréal within 90 days.
The most important prerequisite is the willingness to follow the system. If you do that, the rest takes care of itself.
How much time does this require?
The program is built around 30 to 60 minutes a day. That's the baseline. If you can carve that out consistently, the system works.
Our most successful students usually put in 8 hours or more a week, but that's not the requirement. What matters more than the amount of time is what you're doing with it. Because we've stripped out the research, the guesswork, and the job search grind, every minute you invest moves you closer to an offer.
All live sessions are recorded, and the full library is available on demand, so you can work through things whenever your schedule allows, whether that's before work, during lunch, or late at night.
Is there anything I should prepare before starting?
Not really. When you start, we'll run you through a skillset audit to figure out exactly where you are, and we'll build your roadmap from there.
If you already have a sense of the kind of AI work you want to do, whether that's building on top of LLMs, working on RAG systems, fine-tuning models, or applying AI inside your current company, that's useful information we'll factor in. But if you don't know yet, we'll help you figure it out.
Do you have financing available?
Yes. We offer flexible payment plans so you can spread the cost over the program. We'll walk you through the options during your strategy session.
In many cases, especially for engineers on the Internal Promotion track, companies cover part or all of the program. We'll show you how to make that case to your manager if that's relevant for your situation.
What happens after I finish the program?
For most programs, "finishing" means completing the curriculum. For us, finishing means having a signed offer in hand.
We stay with you through the job search and interview process, and we don't stop until you're hired. If you haven't landed a role within 6 months of us starting to apply on your behalf, you get 100% of your tuition back.
After you land the role, you still have access to the community, the materials, and ongoing support as you ramp up in your new position.
Will I receive a certificate?
Yes, you'll receive a certificate of completion. That said, certificates don't get engineers hired. Production-grade portfolios, senior-reviewed code, and positioning that makes you the obvious choice to hiring managers get engineers hired. That's what we focus on.
Are there hands-on projects?
Yes. The entire program is built around building real systems.
You'll work on production-grade AI applications, with senior technical mentors reviewing your code at every step. By the end, you have a portfolio that demonstrates you can ship, not just learn.
Past students have built domain-specific AI systems, document processing pipelines, classification systems, RAG-based applications, fine-tuned vertical models, and more. The specific projects you work on depend on your target roles and the kind of work you want to do.
What if I have scheduling conflicts with the live sessions?
Not an issue. All live sessions are recorded and available on demand. You can work through the program on your own schedule without losing anything by not attending live.
Do you have a guarantee?
Yes. If you complete the program and don't land an AI engineering role within 6 months of us starting to apply on your behalf, you get 100% of your tuition back.
The reason we can offer this is because we've built the shortest, most direct path from where you are now to an AI engineering offer. If you follow it, the outcome is inevitable. You'll get the full details of what qualifies on your strategy session.
Will you cover multi-modal applications?
Yes. The curriculum covers text, code, SQL, voice, and music across multi-modal architectures. You'll also learn how to fine-tune models within these spaces.
What kind of support do I get outside of the lectures?
You have access to senior technical mentors who review your code, answer your questions, and make sure you never get stuck. On top of that, you get group coaching calls, a community of other engineers going through the same process, and direct support from our curriculum and operations teams.
On the placement side, you get a dedicated Job Search Analyst who handles applications, LinkedIn, resume, and interview prep.
This isn't a watch-videos-and-figure-it-out type of program. You have people on your side at every step.
How does the program stay current?
The AI field moves fast, so our curriculum is built to move with it. Dr. Dipen tracks newly-published research and distills what's actually useful, and since Zao is actively building AI products, the program reflects what tools and techniques are being used in production right now.
The foundational concepts you'll learn, like tokenization, embeddings, attention mechanisms, RAG, and fine-tuning, aren't going anywhere. They're evergreen. What gets updated is the applied layer, which we refresh continuously based on what's actually happening in the field.
What does an AI engineer actually make?
Based on Levels.fyi 2025 data, AI-focused software engineers in the US earn a median of $245,000 per year. At senior and staff levels, the AI premium over non-AI engineers at the same level ranges from 14% to nearly 20%, sometimes higher depending on the company.
At Intuit, for example, AI staff engineers earn close to $917k, compared to $515k for non-AI staff engineers. That gap has been widening, not shrinking.
Do you prep me for interviews?
Yes. Extensively.
We give you a cheat sheet for AI interview questions at top companies, conduct mock technical interviews with subject matter experts, coach you through behavioral and recruiter rounds, and prep you for the kinds of system design and architecture discussions that come up in senior AI roles.
We also help you handle the "walk me through something you built" question, which is where most engineers fumble and lose offers.
Can I send something to my manager to get this approved?
Yes. If you're considering having your company cover the program (which happens a lot, especially on the Internal Promotion track), we have an approval packet that you can hand to your manager. It includes the business case, ROI framing, and talking points you'll need for the conversation.
In some cases, we'll hop on a call with your manager directly to answer their questions. When managers understand what this actually is, that their employee is going to build a real AI system that delivers business value AND level up their skills in the process, they usually say yes.
Become our next success story
The market is hungry for AI engineers, and our system is built to turn you into one. Book your free AI Career Strategy Session and let's map out exactly what your path to an AI engineering role looks like.
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