LiveKitResearch Engineer (Reinforcement Learning)
LiveKit is hiring a Research Engineer (Reinforcement Learning) for a remote role. It pays $135k-$300k a year and Level rates it ; you can apply on LiveKit.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- This is listed as a remote role.
- The posting mentions OpenAI, Llama and vLLM among its tools and skills.
- Level classes it as a senior role.
- LiveKit has 29 other open roles on Level.
AI in this role
Build post-training pipelines, reinforcement learning environments, and evaluations for voice and text AI agents.
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 rate this
Research Engineer (Reinforcement Learning) at LiveKit rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
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?
- Tell me about a project where reinforcement learning was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where synthetic data was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Fine-tuning, Speech, Reinforcement learning, Machine learning, and Synthetic Data. 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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