TL;DR
- Start a Thinking Machines Lab interview plan with the exact vacancy: research, evaluation platforms and training infrastructure require different evidence.
- Prepare one experiment you can defend, one difficult debugging story and one example of changing a decision when the evidence changed.
- Use the original evaluation and failure-recovery exercises below to practise; they are not leaked or reported Thinking Machines interview questions.
- Confirm the actual rounds, format and permitted tools with your recruiter. Public research and job descriptions do not establish a universal interview sequence.
What to prepare for a Thinking Machines interview
Thinking Machines Lab describes an AI research and product organisation working on customisable systems and human-AI collaboration. Its public materials also stress reliable research infrastructure and measuring useful outcomes. That makes a preparation plan focused only on memorised model terminology too narrow.
The official job board, checked on September 14, 2026, separates core engineering, research and research infrastructure. Listed infrastructure specialties include inference, kernels, numerics and training systems. Choose the actual role before allocating preparation time. A kernel engineer and an evaluation-platform engineer may share a mission while needing different examples of strong work. The same principle holds across ML engineer interview loops, where research and applied tracks test different strengths.
This guide concerns Thinking Machines Lab, the AI company. It provides independent practice based on public material, rather than claiming access to the company's interview rubric or a confirmed list of questions. Use your interview invitation as the authority for the format.
Turn the vacancy into an evidence map
The current Evaluation Platform / Infra role describes work across evaluation frameworks, backend systems, data pipelines and interfaces. It calls out reproducibility, provenance, failure recovery and researchers' ability to inspect results. Those responsibilities suggest useful preparation themes; they do not prove that an interviewer will ask a particular system-design question.
Make a three-column note for your own vacancy:
| Responsibility | Evidence from your work | Gap to practise |
|---|---|---|
| Reliable experiments | A run you reproduced and the configuration you retained | Explaining what changed between two results |
| Infrastructure debugging | A failure you narrowed down with logs or measurements | Distinguishing a symptom from a cause |
| Research collaboration | A tool or workflow you changed after user feedback | Defending a useful abstraction without overbuilding |
| Evaluation quality | A metric you questioned or improved | Explaining which conclusion the data does not support |
Replace every example with something you actually did. If the nearest experience is a university or personal project, identify it that way. A small experiment with a clear question and honest limitations is easier to discuss than a large-sounding claim you cannot reconstruct.
Original exercise: an apparent model regression
Two model versions were evaluated on easy and difficult tasks. A dashboard shows that the newer version's overall pass rate fell. A colleague wants to reject the release immediately.
| Evaluation slice | Model A | Model B |
|---|---|---|
| Easy tasks | 90 passes from 100 attempts | 19 passes from 20 attempts |
| Difficult tasks | 6 passes from 10 attempts | 70 passes from 100 attempts |
| Combined | 96 passes from 110 attempts | 89 passes from 120 attempts |
The combined rates are approximately 87.3% and 74.2%. Yet the observed slice rates rise from 90% to 95% and from 60% to 70%. Model B's run contains a much larger share of difficult tasks. The overall comparison therefore mixes a possible model change with a changed evaluation population.
A useful answer does more than name the aggregation problem. Request a comparison on the same held-out items, with matching grading rules and complete run accounting. Inspect whether any tasks disappeared because a worker timed out. Keep the slice-level counts, not only percentages. The small samples also mean that higher observed rates are not, by themselves, proof of a reliable improvement.
For a follow-up, suppose both versions now use the same tasks, but the grader changed halfway through the second run. Preserve the grader version per result and rerun a comparable subset. Explain why simply increasing the number of examples would not fix that systematic mismatch.
Practise ending with a decision: pause the release comparison, repair the evaluation design, and record what evidence would permit a new decision. Avoid concluding that either model is better from the incomplete table.
Original exercise: a reproducible evaluation service
Design a small service that lets researchers submit an evaluation, inspect individual failures and rerun it later. Begin with the identity of a run: dataset revision, task configuration, model or checkpoint identifier, grader revision, relevant dependencies and sampling settings. A seed can help reproduce some experiments, but it is not a universal promise of deterministic distributed execution.
Separate a logical task from an execution attempt. If a worker crashes after storing a result but before acknowledging its message, a retry should not silently count that task twice. A practical design might give the logical task a stable key and each attempt its own record, then choose one accepted result according to an explicit rule. Failed and cancelled work should remain visible rather than disappearing from the denominator.
Ask how a researcher will investigate one surprising score. The interface should connect the aggregate to the task input, model output, grader reasoning where available and relevant errors. Store only material the team is entitled to retain, and apply the same access controls to traces as to their source data.
Now change the requirement: researchers need to add a new kind of task every week. Explain which concepts stay stable and which can be extended. An answer that creates a separate pipeline for every task will become difficult to maintain; an abstraction that prevents useful experiments is also a failure. Name the first concrete use cases and the interface you would defer until a real need appears.
Discuss training and numerical failures methodically
For a role involving training systems or numerics, practise a different original prompt: a run that was stable yesterday now produces non-finite values after a configuration change. Describe how you would narrow the first divergence before changing several settings at once.
Start with the smallest reproducible case, the changed inputs and configuration, and the first point where values become invalid. Compare data batches, loss scaling, precision choices and the relevant operation's inputs. Check whether the problem is deterministic and whether it follows one worker or a particular example. Keep a known-good baseline and avoid presenting a workaround as an explanation.
The Tinker overview is useful context for the company's training interface: it exposes training operations while managing infrastructure. You do not need to buy API access to practise reasoning about experiments, saved state and failure boundaries. A local exercise can test your explanation without pretending to reproduce the company's internal stack.
For a follow-up, explain which state a checkpoint must preserve for your particular training procedure, and how you would validate a resumed run. Distinguish model parameters from optimiser state, data progress and other run metadata. State any nondeterminism you expect instead of promising bit-for-bit equivalence without evidence. For a larger-scale version of this follow-up, a bank of machine learning engineer interview questions includes a training infrastructure design prompt that covers checkpointing and fault tolerance.
Prepare a research discussion that shows judgment
Choose one project and explain the question before describing the model. State the baseline, the comparison, the data boundaries and the result. Then identify one plausible alternative explanation. If you changed the evaluation after seeing results, say so and explain how you prevented that change from becoming an unreported advantage.
Practise these follow-ups:
- Which observation would make you abandon the approach?
- What was the simplest baseline that could have answered the question?
- Which result was surprising, and how did you check it?
- What did another person contribute that your summary must not claim as your own?
- What would you test next with half the compute or half the time?
A strong discussion makes the next experiment understandable. It does not depend on reciting the largest number of recent papers.
Confirm the interview format and rehearse once unaided
Ask the recruiter about the role's technical focus, coding environment, presentation expectations, time allocation and rules for outside tools. Also ask whether there is material you should read in advance. Do not infer the current process from another applicant's role or an undated account.
Finish preparation with an unaided walkthrough of the evaluation table and one truthful project story. Explain the assumptions aloud and leave room for a follow-up that changes them. If you use mock-interview practice, evaluate whether it helps you make that independent explanation clearer. The useful outcome is reasoning you can defend, rather than a polished answer you cannot reproduce.