Technical work simulations
Candidates tackle realistic assignments using role-specific tools and AI coworkers. Your team gets the work, objective checks, and an evidence brief for a more focused interview.
For software engineering, data & analytics, ML engineering, and ML research hiring.
Browse every example without signing up.
Your team defines what it needs to learn.
Investigate, change, verify, and explain.
Inspect the sources. Follow up on the gaps.
Four role areas
Choose work that reflects the role. Inspect how someone investigates a problem, tests an idea, and explains the result.
Software engineering
Investigate a duplicate-write bug, make a change, and add a regression test.
Patch + regression test
What reviewers inspect
Diagnosis, code quality, verification, and operational trade-offs.
Explore the simulationData & analytics
Investigate a metric change, check the data, and explain the conclusion.
Query + decision memo
What reviewers inspect
Data checks, analytical choices, assumptions, and communication.
Explore the simulationML engineering
Investigate a suspicious evaluation result and propose a sound comparison.
Evaluation + model report
What reviewers inspect
Leakage detection, split design, holdout reporting, and model evaluation.
Explore the simulationML research
Reproduce a result within a compute budget, examine the evidence, and recommend whether to adopt the method.
Reproduction + research verdict
What reviewers inspect
Experimental rigor, statistical reasoning, reproducibility, and research judgment.
Explore the simulationEach example includes a task, working context, and a sample evidence brief.
Where it fits
Place a role-specific simulation between the recruiter screen and final interview. Give your panel a shared piece of work to discuss, with specific decisions, trade-offs, and gaps to explore.
Confirm role, motivation, and working constraints.
Observe one meaningful piece of the job under consistent conditions.
Probe the decisions, trade-offs, and gaps visible in the work.
Make the call with the work and reviewer judgment together.
The work informs the conversation. Your team makes the hiring decision. Read our review principles.
SeeTheWork drafts the task and review criteria with your team. Your hiring manager contributes role context and checks that the work reflects the job. Assignments cover software engineering, data & analytics, ML engineering, and ML research.
See the responsibilitiesThe participant session is designed around 90 minutes. Your hiring manager helps refine the task and reviews the resulting evidence. We confirm the schedule and reviewer time in the proposal.
See the pilot scopeAI can be part of the task when permitted. The tool policy is agreed up front and explained before the session. Reviewers discuss the participant’s changes, checks, and reasoning with that context.
Read the candidate and tool policiesAn unanswered question stays visible. Reviewers inspect the source and decide what needs a conversation or another way to assess it. Missing evidence is not treated as proof that someone lacks the skill.
See an unresolved question in the sampleThe starting package includes one assignment, a guided walkthrough, an evidence brief, and a joint review. The written proposal sets the participant count, dates, reviewer time, and price.
Discuss your pilotA focused pilot
Start with a role-specific assignment, a guided walkthrough, and a review package your hiring team can inspect. The first conversation identifies a relevant task and the next steps.
Discuss a pilotTell us what you hire for. We’ll help identify the work to assess.