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LiveKit

Research Engineer (Reinforcement Learning)

Posted Yesterday
Be an Early Applicant
In-Office or Remote
Hiring Remotely in USA
135K-300K Annually
Entry level
In-Office or Remote
Hiring Remotely in USA
135K-300K Annually
Entry level
Build post-training infrastructure for voice and text agents, including training environments, verifiers, synthetic data pipelines, evaluations, and model release gates. Run end-to-end training experiments, select open-weight models, improve tool-using and multi-turn behavior, deploy models to production, and iterate using real-world usage. The role emphasizes Python engineering, reinforcement learning, GPU-based training, data quality, reward design, and production machine learning.
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About LiveKit

LiveKit is building the infrastructure layer for the voice-driven era of computing. Our platform gives developers everything they need to build, test, deploy, scale, and observe agents in production. Founded in 2021, LiveKit powers voice AI applications for OpenAI, xAI, Salesforce, Coursera, Spotify, and thousands of others, collectively facilitating billions of calls each year.

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

  • Competitive salary and equity package

  • Health, dental, and vision benefits

  • Flexible vacation policy

LiveKit is an equal opportunity employer and does not discriminate on the basis of any characteristic protected by applicable law. If you require a reasonable accommodation during the application or interview process, please contact [email protected].

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