AI Research Engineer
Start here. Build forward.
Turn research questions into controlled experiments and reproducible results.
Follow the stages in order, or open a topic to check what you already know. Each panel includes learning content, a practice task and a role checkpoint. Save Learning, Done or Skipped as you go. Skipped topics do not count as completed.
Build your foundations
Python, files and debugging
Understand the concept
Python is the working language for many AI experiments. Learn to inspect small values before connecting a large model. Variables, lists, dictionaries, loops and functions are enough for a first useful script. Errors are information: read the traceback and isolate the smallest failing input.
Learn it step by step
- Create a function with explicit inputs and outputs.
- Read a small JSON file and validate required keys.
- Handle a missing file and malformed input.
- Write checks for normal and empty input.
Build a small proof
Build a script that counts tutorial topics from ten fictional records and reports invalid rows separately.
Ready to move on?
You can explain each line, reproduce one error and fix its cause without hiding the exception. Keep the expected output with the script.
Common mistake
Copying a large generated program without understanding its inputs makes even small changes difficult to verify.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Linear algebra and optimisation intuition
Understand the concept
Vectors, matrices, derivatives and gradients provide the language for model calculations. Begin with small numeric examples and visual intuition. A gradient describes a local direction of change; optimisation uses it to adjust parameters against a loss. It does not guarantee a globally best solution.
Learn it step by step
- Multiply a small matrix and vector by hand.
- Plot a simple quadratic loss.
- Follow several gradient steps.
- Compare a learning rate that converges with one that overshoots.
Build a small proof
Use a spreadsheet or short Python script to minimise a one-variable quadratic and log each step.
Ready to move on?
Explain what the gradient, learning rate and loss each represent in your example.
Common mistake
Memorising symbols without relating them to a calculation makes debugging training much harder.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Probability, uncertainty and sampling
Understand the concept
Statistics helps you distinguish a pattern from noise. Learn averages, spread, distributions, sampling and confidence intervals before interpreting model scores. An estimate from a small or biased sample may not describe the wider population.
Learn it step by step
- Summarise a small synthetic dataset.
- Compare mean and median with an outlier.
- Draw repeated samples and observe changing estimates.
- Explain uncertainty in plain language.
Build a small proof
Simulate repeated coin tosses and plot how the estimated heads rate changes as the sample grows.
Ready to move on?
You can explain why two samples yield different estimates without concluding that one must be fraudulent or broken.
Common mistake
Correlation alone does not prove that changing one variable will cause another to change.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Learn the core skills
What is a neural network?
In simple words
A neural network combines numbers through connected layers to produce an output. Its weights are adjusted during training. Start with one small calculation before thinking about enormous models. The term “neural” is inspired by biology, but it does not establish that the system thinks or feels like a human brain.
Two useful terms
Weight: an adjustable multiplier. Activation: a function applied to a layer’s values.
Build your understanding
A neural network combines weighted inputs through layers and nonlinear transformations. Training adjusts weights to reduce a chosen loss. You can understand the basic computation with one neuron before learning larger architectures. More layers do not remove the need for careful data and evaluation.
Follow the steps
- Choose two inputs, two weights and a bias.
- Compute the weighted sum.
- Apply a simple activation such as ReLU.
- Compare the output with a target and describe the resulting error.
Worked example
x = [2, 3], weights = [0.5, -1], bias = 1
z = 2*0.5 + 3*(-1) + 1 = -1
ReLU(z) = max(0, z) = 0
This is a forward pass, not a complete training algorithm.Read the example once, then explain each part aloud in your own words. If it includes Python, you can understand the idea first and run the code later in a local Python environment. Code is shown as text; this page does not execute it.
Try this yourself
Change the bias to 3 and recompute the output. Explain which part of the computation changed.
Check your understanding
The new weighted sum is 1, so ReLU outputs 1. A bias shifts the activation boundary without changing the input weights.
A mistake to avoid
An analogy to a biological brain is only an analogy. It does not explain reliability, consciousness or general intelligence.
Before looking at the answer, write a short explanation and one example of your own. Finish by naming one limitation of the method. Use Mark as read when you are ready, or return to the previous lesson if a term is unclear.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
What is a transformer in AI?
In simple words
A transformer is a model architecture that uses attention along with other components. Attention helps combine information from different positions in a sequence. In “river bank” and “bank account”, the surrounding words give different clues. This is an intuition for context, not a complete explanation of how a trained model works.
Two useful terms
Architecture: the organisation of a model. Attention: learned computation that weights information from different positions.
Build your understanding
Attention lets a model combine information from different positions using learned relevance weights. Transformers use attention with other components to process sequences. The representation of a word can change with context. This tutorial is an intuition exercise, not a full implementation of training or inference.
Follow the steps
- Write two sentences where the same word has different meanings.
- Identify nearby words that disambiguate it.
- Imagine assigning different relevance weights to those words.
- Explain how a context-sensitive representation differs from a fixed dictionary lookup.
Worked example
"She sat by the river bank."
"She deposited money at the bank."
The tokens "river" and "money" provide different context.
Attention is learned numeric computation, not a human reading rule.Read the example once, then explain each part aloud in your own words. If it includes Python, you can understand the idea first and run the code later in a local Python environment. Code is shown as text; this page does not execute it.
Try this yourself
Use "bat" in a sports sentence and an animal sentence. Identify the evidence that distinguishes the meanings.
Check your understanding
Context can change the useful representation of the same token. Attention weights alone are not a reliable explanation of a model’s complete decision.
A mistake to avoid
Do not interpret a fluent generated continuation as verified knowledge. Sequence modelling and fact-checking are different tasks.
Before looking at the answer, write a short explanation and one example of your own. Finish by naming one limitation of the method. Use Mark as read when you are ready, or return to the previous lesson if a term is unclear.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Read papers and reproduce a baseline
Understand the concept
Research engineering turns a scientific claim into an experiment that can be inspected. Identify the question, method, data, assumptions and evidence. Reproduction starts with a simple baseline and a recorded environment, not immediately adding your own modifications.
Learn it step by step
- Summarise a paper’s claim in your own words.
- Identify the evaluation protocol.
- Reproduce a small feasible baseline.
- Document differences from the original setup.
Build a small proof
Write a reproduction report using public or synthetic data with an explicit compute budget.
Ready to move on?
Another person can see which results were measured, which were quoted and which remain unverified.
Common mistake
Do not claim reproduction when only the code runs. Compare the relevant metric and explain mismatches.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Practise with real decisions
Fine-tuning and adaptation
Understand the concept
Fine-tuning changes a model’s parameters using task-specific training examples. It is different from supplying documents at inference time. First establish whether prompting or retrieval solves the problem. A training dataset needs permission, consistent labels and an evaluation split that reflects intended use.
Learn it step by step
- Define a baseline without tuning.
- Prepare a small, reviewed example set.
- Separate training and held-out evaluation.
- Compare usefulness, regressions and cost.
Build a small proof
Write an experiment plan for consistent support-response formatting using synthetic examples.
Ready to move on?
Your plan has a reason tuning might help and a test that could show it did not. Include cases outside the narrow training pattern.
Common mistake
Fine-tuning is not a dependable way to keep rapidly changing facts current.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Ablations and experimental controls
Understand the concept
An ablation removes or changes one component to test its contribution. Good experiments control other conditions and report variability where practical. If several things change together, it becomes hard to know why the result improved.
Learn it step by step
- Fix the evaluation set and baseline.
- Change one meaningful component.
- Repeat under comparable conditions.
- Report failures and uncertainty as well as wins.
Build a small proof
Compare a retrieval system with and without reranking on the same labelled questions.
Ready to move on?
You can attribute the comparison to one controlled difference and describe the limits of the small test set.
Common mistake
Selecting only the best run exaggerates performance. Document your selection method and all relevant runs.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
What is AI model evaluation?
In simple words
A polished answer is not enough to know whether an AI system works. Evaluation checks specific requirements using examples and scoring rules. For a course assistant, you might check whether it finds the correct course, admits missing information and respects account permissions. Keep failure cases as part of your test collection.
Two useful terms
Test case: an input with an expected behavior. Metric: a defined measurement of performance.
Build your understanding
An AI evaluation checks whether a system meets a defined task. Build examples around realistic successes and failures, not only easy demonstrations. Keep the test set stable enough to compare versions and record the model, prompt and settings used. A single aggregate score can hide critical mistakes.
Follow the steps
- Define success in observable terms.
- Create normal, ambiguous, missing-information and adversarial examples.
- Write expected answers or scoring rules before running the system.
- Compare versions and inspect regressions before accepting a change.
Worked example
FAQ evaluation columns:
question | expected source | required facts | forbidden claim | result
Example: fees unknown | FAQ-3 | say unknown | invented fee | pass/failRead the example once, then explain each part aloud in your own words. If it includes Python, you can understand the idea first and run the code later in a local Python environment. Code is shown as text; this page does not execute it.
Try this yourself
Write five tests for an enrolment assistant. Include a user without Pro access and a request for a nonexistent course.
Check your understanding
A response should not promise enrolment if server-side permissions deny it. Include permission-sensitive cases, not just wording checks.
A mistake to avoid
Do not judge a model solely by another model’s unreviewed score. Human checks and deterministic validation are useful for important requirements.
Before looking at the answer, write a short explanation and one example of your own. Finish by naming one limitation of the method. Use Mark as read when you are ready, or return to the previous lesson if a term is unclear.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Ship, evaluate and improve
Inference efficiency and model trade-offs
Understand the concept
Inference is running a trained model on new input. Latency, memory and throughput depend on model size, context, hardware and serving choices. Compare configurations on the same workload. A faster system is only useful if it still meets quality requirements.
Learn it step by step
- Choose a representative request set.
- Measure warm and cold behavior.
- Compare context lengths and batch sizes.
- Record quality alongside latency and memory.
Build a small proof
Make a small experiment table for a mock or locally available model; do not claim hardware results you did not measure.
Ready to move on?
You can explain a trade-off between response time, resource use and quality based on observed measurements.
Common mistake
A benchmark using tiny prompts may not represent long real user requests.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Git and reproducible experiments
Understand the concept
Version control records changes so you can compare experiments and restore a known working state. A commit should describe a coherent change. Keep source code and small configuration files; credentials and large private datasets need separate handling.
Learn it step by step
- Initialise a repository and add a README.
- Commit a working baseline.
- Create a branch for one experiment.
- Compare the change and document how to reproduce it.
Build a small proof
Store two versions of a prompt or data-cleaning script and explain why the second version exists.
Ready to move on?
Another person can run the baseline from your instructions and identify exactly what changed between versions.
Common mistake
Do not commit secrets, private learner records or generated cache folders. Removing a secret from the latest file does not erase Git history.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Monitoring quality, latency and cost
Understand the concept
AI monitoring needs more than uptime. Track response time, failures, input volume and task quality using a suitable sample of reviewed outcomes. Quality can change when users or source documents change. Log enough to diagnose failures without retaining unnecessary sensitive input.
Learn it step by step
- Define a service-level target.
- Record request outcome and latency.
- Track a stable evaluation set over releases.
- Create an alert and an investigation checklist.
Build a small proof
Produce a small report comparing two releases on success rate, p95 latency and estimated per-request cost.
Ready to move on?
You can tell whether a change improved usefulness while increasing cost or response time, and explain the trade-off.
Common mistake
An average hides slow tails and rare severe failures. Examine distributions and critical error categories.
Your role checkpoint
For the AI Research Engineer path, connect this topic to your capstone: Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Build something you can explain.
Reproduce a small published baseline, run one controlled ablation and report measured results with an explicit compute budget.
Role expectations vary by company. Use this path as a starting point and build evidence of your skills.