Applied Research Intern

Sit at the edge of research and production. You'll take a real workflow from a real team, make its failure modes measurable, construct the reward system, and post-train models that beat the frontier baseline on the work that matters. We hire interns who want to be researchers who ship.

• Evaluation suites built from production traces: deciding what to measure, building the graders, and proving they track real quality.

• Verifiers and reward models. Turning human corrections, outcomes, and standards of good work into training signal.

• Post-training experiments end-to-end: hypothesis, data, SFT/DPO/RL runs, ablations, analysis, and the write-up that defends the win with numbers.

• RL environments and agentic harnesses for the workflows we're specializing models for.

• Reproducing recent post-training, reasoning, and agent papers, and translating the ones that work into our stack.

• Currently a student (BS / MS / PhD) in CS, ML, or an adjacent field, or recently graduated.

• Strong empirical ML background. You've run post-training or RL experiments on LLMs and can explain what worked, what didn't, and why.

• Rigorous about evaluation. You can spot a benchmark that lies and design one that doesn't.

• Comfortable in messy production data. Real traces, not curated datasets, are your raw material.

• Bias to ship. Your experiments end in a deployed model or a killed hypothesis, not just a doc.

• Available for at least 12 weeks at meaningful intensity.

• Publications or OSS in post-training, RL, evals, or agents.

• Experience with verifier models, synthetic data, or RL environments at scale.

• Shipped research into a production system before.

• Strong taste for what the field is over- vs. under-investing in.