A written version of the interactive roadmap above — every station, what you'll learn, and a small thing to build — laid out for reading, reference and search.
Research Foundations Start here
F1. The Research Engineer Role
Intermediate · 35 min
A research engineer builds and trains models and the systems that train them — sitting between the researcher who has the idea and the infrastructure that runs it. Learn how the role differs from an AI/ML engineer (who mostly builds features on existing models) and from a pure research scientist (who focuses on the ideas): the research engineer makes experiments real, fast, and reproducible. This is one of the most in-demand and hard-to-automate technical roles.
Skills: Research engineer vs AI engineer vs scientist · Making experiments real and fast · Reproducibility · Where the role sits
Build it: A researcher has a promising idea but no working code. Explain what a research engineer does to turn it into a run you can trust.
F2. The Math You Actually Need
Intermediate · 55 min
You don’t need to be a mathematician, but research engineering rests on real math: linear algebra (vectors, matrices, the operations behind every layer), calculus and the chain rule (how gradients and backprop work), and probability (distributions, likelihood, the basis of loss functions). Learn enough to read the equations in a paper and understand what the code is actually doing, not just to copy it.
Skills: Linear algebra for ML · Calculus and the chain rule · Probability and likelihood · Reading the math in papers
Build it: Backprop is "just the chain rule." Explain in plain terms how the chain rule turns a loss into a gradient for an early layer.
F3. Deep Learning & PyTorch
Intermediate · 60 min
PyTorch is the research engineer’s primary tool, and deep-learning fundamentals are the material. Learn the core: tensors and autograd, building models as modules, the training loop (forward, loss, backward, step), and how optimizers and learning rates actually behave. Build a model from scratch so you understand every line — the intuition you get from doing it by hand is what lets you debug the hard cases later.
Skills: Tensors and autograd · Models as modules · The training loop · Optimizers and learning rates
Build it: Write, in pseudocode, the minimal PyTorch training loop and name what each of the four core steps does.
F4. The Transformer, Deeply
Advanced · 55 min
The Transformer is the architecture behind modern AI, and a research engineer must understand it to the bone. Learn self-attention (why it works, the math), multi-head attention, positional encoding, the full block, and how the pieces compose into an LLM. Implement attention yourself — the difference between using a Transformer and understanding one is exactly the difference between an applied engineer and a research engineer.
Skills: Self-attention math · Multi-head attention · Positional encoding · Implementing attention yourself
Build it: Explain why attention is O(n²) in sequence length, and what that means for training on long contexts.
F5. Reading Papers
Intermediate · 45 min
Research moves through papers, and reading them well is a core skill. Learn to read efficiently (abstract → figures → method → results), to extract the actual contribution from the framing, to judge a paper critically (is the comparison fair, are the results significant?), and to keep up with a fast field without drowning. A research engineer who reads papers sharply turns the frontier into things they can build.
Skills: Efficient paper reading · Extracting the real contribution · Critical judgment · Keeping up without drowning
Build it: A paper claims a big win. List the three things you’d check before believing the result is real and reproducible.
F6. Reproducing Results
Advanced · 55 min
The defining research-engineer skill is turning a paper into working, verified code — reproducing a result from scratch. Learn to translate a method into an implementation, to match the setup precisely, to debug the inevitable gaps between paper and reality, and to know when your reproduction is genuinely correct. Reproducing results is the best way to learn deeply, and it’s exactly what the role is paid to do.
Skills: Paper → implementation · Matching the setup · Debugging paper-vs-reality gaps · Knowing it’s correct
Build it: Your reimplementation gets worse numbers than the paper. List the usual suspects you’d check, in order.
Training at Scale Make it real
T1. Data Pipelines
Advanced · 50 min
Models are only as good as their data, and building the data pipeline is a huge part of real research engineering. Learn to source, clean, deduplicate, filter, and tokenize data at scale, why data quality often matters more than model tweaks, and how to build pipelines that feed training efficiently. The unglamorous data work is frequently where the actual results come from.
Skills: Sourcing and cleaning at scale · Dedup and filtering · Tokenization · Data quality over model tweaks
Build it: A model underperforms despite a good architecture. Explain why the data pipeline is the first place a research engineer looks.
T2. Training Dynamics
Advanced · 55 min
Getting a model to train well is an art built on understanding. Learn the dynamics: how learning rate schedules, batch size, initialization, and normalization interact; what loss curves tell you; and the common failure modes — divergence, instability, plateaus. A research engineer reads a training run like a doctor reads vitals, diagnosing what’s wrong and what to change.
Skills: LR schedules and batch size · Initialization and normalization · Reading loss curves · Divergence and instability
Build it: A training run’s loss spikes and never recovers. Walk through the likely causes and what you’d change first.
T3. Distributed Training
Advanced · 60 min
Real models don’t fit on one GPU, so scaling training across many is core research-engineering craft. Learn the parallelism strategies — data, tensor, pipeline, and sharding (ZeRO/FSDP) — what each trades off, how communication between GPUs becomes the bottleneck, and how to actually run a multi-GPU or multi-node job. This is where research engineering meets serious systems work.
Skills: Data / tensor / pipeline parallelism · Sharding (ZeRO / FSDP) · Communication bottlenecks · Multi-GPU / multi-node runs
Build it: A model is too big for one GPU’s memory. Explain the difference between data parallelism and sharding for solving that.
T4. Scaling Laws
Advanced · 45 min
Scaling laws are one of the most important ideas in modern ML: model performance improves predictably with model size, data, and compute, and there’s a compute-optimal balance between them. Learn what the laws say (and their limits), how to use them to plan experiments and allocate a compute budget, and why they reshaped how frontier models are built. Reasoning in terms of scaling is central to research-engineering judgment.
Skills: Model / data / compute scaling · Compute-optimal balance · Planning with a compute budget · The laws’ limits
Build it: You have a fixed compute budget. Explain how scaling-law thinking guides the trade-off between a bigger model and more data.
T5. Experiment Tracking
Intermediate · 40 min
Research is a stream of experiments, and without discipline it becomes chaos you can’t learn from. Learn to track experiments rigorously — configs, metrics, and artifacts versioned so every run is reproducible and comparable, to change one thing at a time, and to build the infrastructure that lets a team run and compare hundreds of experiments. Good experiment hygiene is what turns activity into knowledge.
Skills: Tracking configs, metrics, artifacts · Reproducible, comparable runs · One change at a time · Experiment infrastructure
Build it: Two runs give different results and no one recorded what differed. Describe the tracking that would have made the comparison meaningful.
T6. Debugging Training
Advanced · 50 min
Training bugs are uniquely nasty — silent, expensive, and often only visible in the results. Learn to debug systematically: verify data and shapes, check gradients and numerics (NaNs, overflow), isolate on small scales before big runs, and reason about whether a bad result is a bug or a genuine finding. The research engineer who debugs training well saves enormous time and compute, and that skill is deeply hard to automate.
Skills: Verifying data and shapes · Gradient and numerical issues · Small-scale isolation · Bug vs genuine finding
Build it: Your loss is NaN after a few steps. List the systematic checks you’d run, cheapest first.
Frontier & the Craft Own the future
P1. Post-Training & Alignment
Advanced · 55 min
A pretrained model is raw capability; post-training makes it useful and aligned. Learn the modern stack: supervised fine-tuning, RLHF and its successors (DPO and beyond), instruction tuning, and how these shape a model’s behavior. Understand what each stage does, the data it needs, and the trade-offs — post-training is where much of the frontier research and the practical value now lives.
Skills: Supervised fine-tuning · RLHF, DPO and successors · Instruction tuning · Shaping model behavior
Build it: A base model is capable but unhelpful and unsafe. Explain what post-training stages you’d apply and in what order.
P2. Evaluation
Advanced · 50 min
You can’t improve what you can’t measure, and evaluating models honestly is one of the hardest research problems. Learn benchmark design and its pitfalls (contamination, gaming, benchmarks that don’t measure what matters), how to build evals that actually track the capability you care about, and why "the number went up" can be misleading. Rigorous, skeptical evaluation is a defining mark of a good research engineer.
Skills: Benchmark design and pitfalls · Contamination and gaming · Evals that track real capability · Skepticism about numbers
Build it: A model tops a benchmark but feels worse in use. List the ways a benchmark can lie and how you’d evaluate more honestly.
P3. Efficiency
Advanced · 50 min
Frontier work is bounded by compute and memory, so efficiency is core craft. Learn the techniques: mixed precision, quantization, distillation, activation checkpointing, efficient attention, and kernel-level optimization. Understand the trade-offs between speed, memory, and quality, and how to get more capability from a fixed budget. Efficiency is often what turns a research idea into something that can actually be trained or served.
Skills: Mixed precision and quantization · Distillation · Efficient attention and checkpointing · Speed/memory/quality trade-offs
Build it: A model trains but is too expensive to serve. List three efficiency techniques and what each trades away.
P4. The Research Process
Advanced · 45 min
Beyond skills, research is a way of working: forming a clear hypothesis, designing an experiment that could actually disprove it, running it cleanly, and reasoning honestly about what the result means. Learn to avoid the traps — fooling yourself, chasing noise, confirmation bias — and to iterate from a question to a real finding. This scientific discipline is what separates research from tinkering, and AI can’t supply it.
Skills: Hypothesis-driven work · Experiments that can disprove · Honest reasoning about results · Avoiding self-deception
Build it: An experiment "worked." Describe how you’d check whether it’s a real finding or you fooled yourself.
P5. Research to Production
Advanced · 45 min
A result in a notebook isn’t impact — the research engineer often bridges to production, turning a working experiment into a model that ships. Learn what changes at that boundary: robustness, serving efficiency, monitoring, and the collaboration with applied and infra teams. Being able to take research all the way to a deployed model is a rare, high-value combination and increasingly what the role is asked to do.
Skills: Experiment → shippable model · Robustness and serving · Monitoring in production · Working with applied/infra teams
Build it: Your research model beats the current one offline. Describe what you’d validate before it could replace production.
P6. The Research Engineer Career
Advanced · 40 min
The path branches: deeper into research (toward research scientist), deeper into systems (training infrastructure, ML systems), or into applied leadership. Learn where the role goes, how it differs from and connects to the applied AI-engineer track, what a strong portfolio looks like (reproductions, a real contribution, open-source work), and how the field’s pace makes continuous learning the job. Decide the direction deliberately and build the evidence for it.
Skills: Research scientist / ML systems / applied paths · Vs the applied AI-engineer track · A portfolio of real work · Continuous learning as the job
Build it: Write the six-month plan that moves you from reproducing others’ work to making one small original contribution.