# Applied Research Intern at NextToken

- Company: NextToken
- Location: Mountain View, CA
- Type: Internship · Internship
- Posted: 2026-10-24
- Apply: https://jobs.ashbyhq.com/nexttoken/f202a966-912e-40b8-b55a-8cca851eef5e/application?embed=true
- Page: https://newgrad.ai/jobs/applied-research-intern-f72f404c

## Summary by newgrad.ai

What you'll do:

- Develop evaluation suites using production data to measure model quality.
- Create reward models and verifiers to improve training signals.
- Execute end-to-end post-training experiments and analyze results.
- Build RL environments and implement recent research into production.

What they're looking for:

- Students or recent graduates in CS, ML, or related fields.
- Experience running post-training or RL experiments on LLMs.
- Ability to design rigorous evaluation methods for messy production data.
- Commitment to a minimum 12-week internship duration.
- Preference for candidates with research publications or open-source contributions.

## Posting

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.

### What you'll own

- 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.

### About you

- 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.

### Bonus

- 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.

From NextToken's public posting, laid out for reading by newgrad.ai with the wording unchanged. Check the original before you apply.

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More new-grad and intern roles: https://newgrad.ai/llms.txt