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, Enterprise Lead are senior technical leaders accountable for the technical success of Distyl’s most complex enterprise AI deployments. They may own one large customer engagement or provide technical oversight across multiple smaller accounts, maintaining technical coherence across workstreams, developing technical leaders, and ensuring production AI systems deliver durable business value in complex customer environments.

This role is for leaders who can operate across altitudes: reasoning with customer executives about tradeoffs and delivery risk, setting architecture across multiple teams, developing AI Engineer Leads and senior engineers, and staying close enough to the work to make sound technical judgments about AI system behavior, code quality, evaluations, integrations, and production reliability.

Key Responsibilities

Lead the technical success of complex enterprise AI deployments, either across one large customer engagement or a portfolio of smaller accounts, often spanning multiple use cases, production systems, workstreams, and teams

Set technical direction across engagements, ensuring architecture, system behavior, evaluation strategy, integrations, and operational practices remain coherent as deployments grow

Develop AI Engineer Leads, senior ICs, and emerging technical leaders, raising the bar for technical judgment, code quality, customer communication, and production ownership

Serve as the senior technical partner to customer executives, enterprise architects, and business partners, building trust through clear communication, sound judgment, and reliable delivery

Maintain technical proximity to live systems by reviewing designs, interrogating system behavior, debugging critical issues, guiding evaluation strategy, and participating in incident reviews and production quality improvements

Use AI-native engineering practices to increase your own leverage and model effective use of AI tools for design, implementation, debugging, analysis, experimentation, and operational improvement

Partner with company leadership on staffing, organizational design, leadership development, and customer expansion as engagements grow from pilot to enterprise-scale deployment

What We Require

8+ years of software engineering experience, including experience leading technical teams through ambiguous, high-stakes production delivery

Multi-team technical leadership. You have led technical execution across multiple teams, workstreams, accounts, or senior engineers. You know how to create clarity, set standards, develop leaders, and keep teams aligned without becoming a bottleneck

Talent development. You raise the capability of the engineers around you. You can coach Tech Leads, senior ICs, and emerging leaders, and you know how to build a talent-dense engineering team over time. Prior people management experience is strongly preferred, but not required if you have demonstrated organizational leadership across multi-team engineering efforts

Enterprise technical ownership. You take responsibility for whether complex technical deployments deliver durable customer value. You can reason across architecture, delivery risk, team capability, customer constraints, system behavior, and production operations