Distyl AI
Research Engineers, Data
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 build data systems that power reliable AI workflows across enterprise environments
- Develop pipelines for collecting, cleaning, transforming, labeling, and evaluating domain-specific data used by AI systems
- Build tools and workflows that help teams turn raw customer data into usable context for retrieval, evaluation, reasoning, and execution
- Partner with AI Researchers and AI Engineers to understand how data quality affects system behavior and production outcomes
- Develop synthetic data, annotation, and feedback-loop strategies to improve system performance in areas where real-world data is sparse or noisy
- Strong Data Engineering Fundamentals: You write clean Python and SQL, understand data modeling and pipeline reliability, and can build systems that are maintainable under production constraints
- 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.
Requirements
- Research-Oriented Builder: You are comfortable investigating how data quality, structure, and representation affect AI system performance
Benefits
- Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.
- Create data quality frameworks that identify coverage gaps, ambiguity, drift, duplication, leakage, and other failure modes
This listing is sourced directly from Distyl AI's careers page and normalized into a canonical job model.