Research Engineer (Reinforcement Learning)

Overview

Job Description

Responsibilities

• - Build the environments and verifiers our models train against
• - Own the synthetic data pipeline, from generation through the quality gates
• - Run training experiments end to end, and explain what moved the model
• - Build the evaluations a release has to clear
• - Choose and adapt open-weight base models for our tasks
• - Make trained behavior hold up for voice and text agents alike
• - Ship models into production and keep improving them on real usage

Required Skills

• - A strong Python engineer
• - Have carried a model from raw data through to production
• - Treat data as the product: coverage, diversity, leakage
• - Assume a model will exploit a weak reward, and design against it
• - Comfortable with GPUs and honest about their limits
• - Know when to train, and when not to
• - Comfortable working collaboratively in a remote environment
• - Nice to Have: Experience with post-training: fine-tuning, reward design, or reinforcement learning such as GRPO RL and fine-tuning frameworks such as TRL, verl, or OpenRLHF, or a training loop you wrote yourself
• - Fast rollouts with vLLM or SGLang, multi-GPU training with FSDP
• - Training tool-using or multi-turn agents
• - Execution sandboxes, verifiers, eval harnesses, or tooling other engineers depend on
• - Open-weight families such as Qwen or Llama, LoRA and similar

Benefits

• - Competitive salary and equity package
• - Health, dental, and vision benefits
• - Flexible vacation policy