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Research Engineer, RL Frontiers

Etched · San Jose · On-site

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About Etched

Co-designed chips, racks, and software to push the pareto frontier to best-in-class throughput and interactivity. Backed by Kleiner Perkins and Sequoia.

About the role

Etched is building AI systems that accelerate its own development. As a Research Engineer on the RL Frontiers team, you will train our proprietary frontier models to automate kernel development, ASIC engineering, production pipelines, and 3-D hardware operating systems. You will use our proprietary simulators and environments to create custom data mixes and post-train models to SOTA performance on our hardest engineering tasks. Found on 1752vc Careers, the job board for startup and VC roles.

What they're looking for

  • RL or post-training frameworks (e.g., verl, OpenRLHF, TRL, prime-rl) or inference engines (vLLM, SGLang)
  • Agentic, multi-turn RL with verifiable rewards
  • Distillation and training small, specialized models for production
  • ASIC design or verification, accelerator kernels, or large-scale compute infrastructure
More about this role

Etched is building AI systems that accelerate its own development. As a Research Engineer on the RL Frontiers team, you will train our proprietary frontier models to automate kernel development, ASIC engineering, production pipelines, and 3-D hardware operating systems.

You will use our proprietary simulators and environments to create custom data mixes and post-train models to SOTA performance on our hardest engineering tasks.

Own the post-training stack completely end to end.

Build RL environments with ASIC, kernel, and supercomputing engineers, rewarded by simulation, formal tools, tests, and hardware measurements.

Create custom data mixes that unlock frontier performance on relevant tasks.

Create benchmarks that drive real productivity in the models and avoid reward hacking

Have hands-on post-training experience, including SFT and RL (e.g., PPO, GRPO), and have shipped models people used.

Have taken a model from bad to good on a task it wasn’t built for, ideally with scarce data, for example as an early post-training hire at an applied AI company.

Are a strong PyTorch and distributed-training engineer who reads the rollouts and checks the numbers before trusting a...

Read the full posting on Etched's site ↗

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