LiveKitResearch Engineer (Reinforcement Learning)
AI in this role
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 recruiting@livekit.io.
How we score this
Research Engineer (Reinforcement Learning) at LiveKit scores 96 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI Level 4. Building AI systems is the job itself: without AI, the role would not exist.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- What have you built with speech recognition or text-to-speech, and where did it break?
- What are the limits of OpenAI that you've run into, and how did you work around them?
- What's a project where you used Llama hands-on?
- Walk me through how you've used vLLM in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Fine Tuning, Speech, OpenAI, Llama, and vLLM. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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