Distyl AI
Research Engineers, Post-Training
San Francisco
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About the role
- ABOUT DISTYL AI Distyl is an applied AI technology company partnering with the world's most ambitious institutions to rearchitect critical operations for the frontier of AI.
- As a result, Distyl's technologies affect some of the world's largest operations - from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.
- Research Engineers operate at the intersection of applied research, systems engineering, and customer-facing deployment.
Responsibilities
- Design and run post-training workflows that improve the behavior, reliability, and usefulness of AI systems
- Develop datasets, preference signals, evaluation suites, reward models, fine-tuning workflows, and feedback loops for applied AI use cases
- Build infrastructure for experimentation, model comparison, regression testing, and behavior analysis
- Partner with AI Researchers to explore new post-training methods and with AI Engineers to apply successful techniques in deployed systems
- Create repeatable processes for adapting AI systems to customer domains while preserving robustness, transparency, and maintainability
- We research and deploy technologies that power AI-native operations - both for our partners and for Distyl itself.
- Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s.
Skills
- AI-Native Working
- Research-Oriented Builder: You care about understanding why behavior changes, not just whether a benchmark improves
Benefits
- Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.
- Experience Improving Model Behavior: You have worked with fine-tuning, preference optimization, reinforcement learning, reward modeling, synthetic data, evals, or related post-training techniques
This listing is sourced directly from Distyl AI's careers page and normalized into a canonical job model.