AI Engineer

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

AI Engineers build and operate production AI systems that deliver business value inside customer environments. This role is for engineers who thrive in ambiguous problem spaces, take ownership of outcomes, and want to work directly on AI systems that must perform reliably under enterprise constraints.

AI Engineers are hands-on builders. They design, implement, deploy, and iterate on end-to-end AI systems in close partnership with customers, subject matter experts, and other Distyl engineers. They translate messy operational needs into concrete system behavior, build the software and AI workflows required to support that behavior, and continuously improve systems through evaluation, feedback, integration, and production iteration.

This is not a demo-building role. AI Engineers are expected to make AI systems work in practice: with users, data, constraints, and accountability for production outcomes.

KEY RESPONSIBILITIES

- Build and operate AI systems deployed in customer environments, taking ownership of system behavior, reliability, and usefulness in production

- Design and implement compound AI workflows that combine models, prompts, agents, tools, retrieval, evaluation, feedback loops, and execution into coherent production systems aligned with user and SME needs

- Develop clean, maintainable Python services and application logic that integrate AI capabilities into customer workflows, data platforms, APIs, and existing applications

- Operate on live systems by measuring behavior, identifying failure modes, debugging issues, and iterating rapidly to improve quality, reliability, and user value

- Build evaluation frameworks, test cases, feedback mechanisms, and observability patterns that help teams understand and improve AI system performance over time

- Work directly with customer stakeholders and subject matter experts to understand workflows, clarify requirements, reason about tradeoffs, and adapt systems as needs evolve