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. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business. What We Are Looking For Lead Forward Deployed AI Engineers are hands-on technical leaders who own the architecture, execution, and delivery of critical production AI systems for large enterprise customers. They make technical decisions, lead project delivery, coach engineers, and partner closely with customer stakeholders and Distyl’s AI strategy team to ensure systems deliver real business value. This role is for engineers who want ownership over production outcomes: shaping system architecture, leading technical execution, working directly with customers, and improving AI systems under real-world constraints. Key
Responsibilities Lead and mentor a team of Forward Deployed AI Engineers—owning technical direction, code quality, execution standards, and individual growth Design and implement AI systems that combine models, agents, retrieval, evaluation, and execution into coherent, production-ready systems aligned with real business outcomes Work directly with customer stakeholders, often in high-visibility settings, and communicate clearly about system behavior, tradeoffs, limitations, and paths to improvement Operate and improve live AI systems by measuring behavior, identifying failure modes, debugging issues, and rapidly iterating on quality, reliability, and usefulness Integrate AI systems into customer data platforms, APIs, and existing applications. Make pragmatic system design decisions that balance speed, robustness, maintainability, and long-term operability Take accountability for outcomes in production and adapt systems as
