5-7 years of hands-on experience in reinforcement learning (RL) or preference learning.
Proven ability to train model weights and measure behavioral changes.
Experience building scalable RL pipelines that autonomously generate questions and tasks.
Background in developing RL systems for chatbot or conversational agents in a corporate setting.
Bonus: Familiarity with audio or speech post-training and large-scale distributed training.
Responsibilities
Own the RL aspect of post-training for a text-to-speech model.
Design and implement PPO, GRPO, DPO, or on-policy loops on model weights.
Build complex agent loop systems to generate training signals for RL.
Create pipelines that autonomously write questions and tasks for scalable training.
Collaborate with speech researchers to ensure text-based developments translate to audio.
Benefits
Healthcare, dental, and vision coverage.
Equity options available.
Flexible work environment with hybrid or fully remote options.
Full Job Description
Agentic RL researcher, post-training (San Francisco or remote)
The role
You'll own the RL side of post-training the next text-to-speech model. A big part of how that model gets good is how it's post-trained, which means building complex, text-based agent loop systems that generate the training signal for RL. You'll design and run PPO, GRPO, DPO or on-policy loops on model weights, with the environments and rewards that go with them. You'll also build pipelines that write their own questions and tasks, so training scales without constant manual data curation. Ablations show what moved behaviour, and you'll work with the speech researchers so what you build in text carries over to audio.
What we're looking for
Hands-on RL or preference learning that trained model weights and changed behaviour in a way you can measure.
You've built or led an RL pipeline that scales, ideally one that generates its own questions and tasks (curating an SFT dataset on its own isn't what this role needs).
You've done it at a real company, with real constraints, on chatbot-style or conversational agents.
Bonus: audio or speech post-training, large-scale distributed training, and published or open-source work in LLM post-training.
Salary and benefits
Up to around $250k depending on experience, plus equity. Healthcare, dental and vision.
Logistics
Full-time, permanent. San Francisco hybrid or fully remote. Start date ASAP.
