Research Engineers, Data

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. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.

We research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. 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.

Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s.

WHAT WE ARE LOOKING FOR

At Distyl, Research Engineers build the bridge between frontier AI research and production systems that deliver real business value. This role is for engineers who are excited to investigate how AI systems should be designed, rapidly prototype new ideas, and turn promising concepts into reliable systems that work inside real customer environments.

Research Engineers operate at the intersection of applied research, systems engineering, and customer-facing deployment. They design and implement compound AI systems, run experiments to understand system behavior, build evaluation frameworks, and collaborate closely with AI Researchers, AI Engineers, and customer stakeholders. Their work is not limited to demos or isolated prototypes: they help turn new techniques into robust systems that can be measured, operated, and improved in production.

KEY 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

- Create data quality frameworks that identify coverage gaps, ambiguity, drift, duplication, leakage, and other failure modes

- 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

- Analyze customer workflows and datasets to determine what information AI systems need, where that information should come from, and how it should be represented

- Communicate clearly with internal teams and customer stakeholders about data assumptions, limitations, risks, and tradeoffs