Forward Deployed Engineer

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 At Distyl, Forward Deployed AI Engineers build and operate AI systems that deliver real business value inside customer environments. This role is for engineers who thrive in ambiguous problem spaces, take ownership of outcomes, and are excited to work directly on production AI systems that must perform reliably under real-world constraints. Forward Deployed AI Engineers are hands-on builders who design, deploy, and iterate on end-to-end AI systems. They work close to customers to understand real requirements, translate them into concrete system designs, and continuously improve system behavior through evaluation, iteration, and integration. Their focus is not on demos or experiments in isolation, but on making AI systems work in practice. KEY RESPONSIBILITIES - Own the behavior and performance of AI systems deployed for customers - Design and implement compound AI workflows that combine models, agents, retrieval, evaluation, and execution into coherent, production-ready systems aligned with real SME needs - Operate on live systems, measuring behavior, identifying failure modes, and iterating rapidly to improve quality, reliability, and usefulness. This includes designing prompts and agent logic, building evaluation frameworks, integrating feedback loops, and using AI-driven tools to accelerate debugging and experimentation - Integrate AI systems into customer data platforms, APIs, and existing applications. You'll make pragmatic system design decisions that balance speed, robustness, and maintainability, and ensure systems remain understandable and operable over time - Work directly with customer stakeholders—often in high-visibility settings—and are expected to reason clearly about system behavior, tradeoffs, and limitations. - Take accountability for outcomes in production and adapt systems as requirements evolve WHAT WE REQUIRE - 2+ years of software engineering experience - Ownership Mentality for AI Systems: You take responsibility for whether an AI system actually delivers its intended value in production. You are comfortable making independent technical decisions across system design, evaluation, and integration, and owning the results of those decisions - Experience Building AI Systems, Not Just Calling APIs: Built applications powered by LLMs or other AI models and are comfortable composing multiple components—prompts, agents, tools, retrieval, evaluators—into end-to-end systems. You reason about system behavior holistically rather than treating models as black boxes - Strong Engineering Fundamentals: Write clean, maintainable Python and are comfortable building and operating real systems. You understand core engineering concepts like versioning, debugging, testing, and performance, and can build systems that hold up under real usage and scrutiny - AI-Native Working Style: Us