Research Engineer (Reinforcement Learning)

About This Role

We are looking for an exceptional engineer to build post-training at LiveKit. Our agents run over voice and increasingly over text channels like SMS and chat, and the interesting problems show up over long horizons: staying useful across many sessions, working with context that accumulates over time, and using tools reliably in the middle of a live conversation.

What You'll Do

• 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

Who You Are

• 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

Our Commitment to You

• The opportunity to shape the brand of a fast-growing developer platform
• Collaboration with a small, senior team that deeply values craft and creativity