AI / ML Engineer

Engineering AI Technical Paid assignment

A structured process for AI and ML engineers built around a paid applied-ML take-home scored against an evaluation rubric.

Why this works:

Portfolio and model links in the application surface shipped work before the first call
A paid take-home with its own eval suite separates engineers who ship from candidates who only talk fluently about AI
Rubric-scored team review keeps the decision anchored to the work, not to whoever spoke last in the debrief
The technical deep-dive probes the cost-latency-quality tradeoff that production AI systems live on

Best for: Startups making their first AI/ML hire to ship features on foundation models
Timeline: ~2 weeks
Candidate effort: 4-6 hours (paid)

Process stages

This template includes 7 stages that candidates move through:

Application

Submit Application

Tell us about the AI systems you’ve shipped and share links to work we can see: a repo, a live demo, a model card, or a technical write-up.

Applied ML Take-Home

Code Assignment

Build a small working AI feature, such as a RAG pipeline or a tool-using agent, and ship the eval suite that proves it works. We score every submission against a shared rubric: task quality, eval design, code quality, and the cost and latency numbers you hit. The assignment is paid ($600 USD) regardless of outcome.

USD 600

Rubric Review

Team Review

Our team scores your submission against the shared rubric and reviews your eval results together.

Technical Deep-Dive

Interview

A working session on your take-home: we extend it together and dig into cost, latency, and quality tradeoffs, eval design, and how you’d monitor this system in production.

75 min

Product & Culture

Interview

Meet the team and talk through how you turn a vague product idea into an AI feature worth shipping.

45 min

References

Reference Check

We’ll reach out to your references for a brief conversation.

Offer

We’ll present you with an offer to join the team.