Research Engineer, RL Frontiers for Frontier AI

Etched.ai; San Jose (CA); On-site; USD 360,000 - 440,000; Full time

Benefits offered by this job

Medical, dental, and vision packages

Housing subsidy

Relocation support

Wellness benefits

Daily lunch and dinner in our office

Unlimited compute budget

Job summary

Etched.ai is building frontier AI systems in San Jose, aimed at advancing RL-centric development for kernel, ASIC, and production pipelines. You will train frontier models to automate complex hardware and software tasks using proprietary simulators and environments.

You will own the post-training stack end to end, build RL environments with hardware engineers, and craft data mixes and benchmarks to push state-of-the-art performance while ensuring ROI.

Qualifications

• Hands-on post-training experience, including SFT and RL (e.g., PPO, GRPO), and shipped models.
• Taken a model from bad to good on a task it wasn’t built for, ideally with scarce data, as an early post-training hire.
• Strong PyTorch and distributed-training engineer who checks rollouts and numbers before trusting results.
• Understand systems RL well enough to speed up loops, not just improve quality.
• Entrepreneurial and capable of making good calls under compute and data constraints.

Responsibilities

• 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.

Skills

Post-training RL

SFT

PPO/GRPO

PyTorch

Distributed training

Model evaluation

System optimization

Tools

verl

OpenRLHF

vLLM

SGLang

TRL

Job description

Etched.ai is building frontier AI systems in San Jose, aimed at advancing RL-centric development for kernel, ASIC, and production pipelines. You will train frontier models to automate complex hardware and software tasks using proprietary simulators and environments.

You will own the post-training stack end to end, build RL environments with hardware engineers, and craft data mixes and benchmarks to push state-of-the-art performance while ensuring ROI.