Research Engineer (Reinforcement Learning) - Build Post-Training at LiveKit | Torre

Research Engineer (Reinforcement Learning) - Build Post-Training

Emma highlights
This highlight was written by Emma’s AI. Ask Emma to edit it.
Full-time

Legal agreement: To be defined

Provide your expected compensation while applying
location_on
Remote (anywhere)
Shared by
Emma of Torre.ai
about 6 hours ago

Responsibilities


About LiveKitLiveKit 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 RoleWe 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 DoBuild the environments and verifiers our models train againstOwn the synthetic data pipeline, from generation through the quality gatesRun training experiments end to end, and explain what moved the modelBuild the evaluations a release has to clearChoose and adapt open-weight base models for our tasksMake trained behavior hold up for voice and text agents alikeShip models into production and keep improving them on real usageWho You AreA strong Python engineerHave carried a model from raw data through to productionTreat data as the product: coverage, diversity, leakageAssume a model will exploit a weak reward, and design against itComfortable with GPUs and honest about their limitsKnow when to train, and when not toComfortable working collaboratively in a remote environmentNice to HaveExperience with post-training: fine-tuning, reward design, or reinforcement learning such as GRPORL and fine-tuning frameworks such as TRL, verl, or OpenRLHF, or a training loop you wrote yourselfFast rollouts with vLLM or SGLang, multi-GPU training with FSDPTraining tool-using or multi-turn agentsExecution sandboxes, verifiers, eval harnesses, or tooling other engineers depend onOpen-weight families such as Qwen or Llama, LoRA and similarOur Commitment to YouThe opportunity to shape the brand of a fast-growing developer platformCollaboration with a small, senior team that deeply values craft and creativityCompetitive salary and equity packageHealth, dental, and vision benefitsFlexible vacation policy