Job description

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 - Use AI-native engineering tools to accelerate implementation, debugging, experimentation, data analysis, and system improvement - Collaborate with other AI Engineers, AI Strategists, and other Distillers to make pragmatic system design decisions that balance speed, robustness, maintainability, and customer impact - Take accountability for the production outcomes of the components, workflows, and systems you build WHAT WE REQUIRE - 5+ years of software engineering experience - Ownership mentality for AI systems. You take responsibility for whether the systems you build deliver their intended value in production. You are comfortable making technical decisions, learning from system behavior, and owning the results of your work - Experience building AI systems. You have built applications powered by LLMs or other AI models and are comfortable composing multiple components — prompts, agents, tools, retrieval, evaluators, workflows, and integrations — into end-to-end systems. You reason about system behavior holistically rather than treating models as black boxes - Strong engineering fundamentals. You write clean, maintainable Python and are comfortable building production software systems. You understand core engineering concepts like versioning, debugging, testing, performance, code review, and production readiness - AI-native working style. You use AI tools daily to write and debug code, explore designs, analyze data, and automate repetitive work. You are curious about new model capabilities and techniques, and actively incorporate them into how you build and iterate on systems - Comfort in customer environments. You are able to work directly with customer teams, ask good questions, and adapt quickly to new domains. You communicate clearly about system behavior, limitations, and tradeoffs, and can operate effectively in high-trust, high-visibility situations - Pragmatic delivery mindset. You can navigate ambiguity, make progress with incomplete information, and balance speed with robustness when building systems that need to work for production users - Willingness to travel. Travel is typically 10–30%, depending on the project, customer needs, and your role on the engagement WHAT WE OFFER - The base salary range for this role is $180K – $250K, depending on experience, location, and level. In addition to base compensation, this role is eligible for meaningful equity, along with a comprehensive benefits package - 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible time off - Retirement and financial planning benefits, including access to pre-tax HSA, FSA, and commuter accounts, 401(k), and financial coaching resources - Comprehensive wellness benefits, including physical fitness, mental well-being, and fertility and family-building benefits through Carrot - Complimentary in-office lunches and snacks provided - Access to state-of-the-art AI models, generous usage of modern AI tools, and real-world business problems - Ownership of high-impact projects across top enterprises - A mission-driven, fast-moving culture that values curiosity, pragmatism, and excellence Distyl has offices in San Francisco and New York. This role follows a hybrid collaboration model with 3+ days per week (Tuesday–Thursday) in‑office. #LI-Hybrid We believe diverse perspectives make our work stronger and more impactful. We are an equal opportunity employer and evaluate all applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other legally protected characteristic. We encourage candidates from all backgrounds to apply.

Requirements

Must-haves

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.
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
Use AI-native engineering tools to accelerate implementation, debugging, experimentation, data analysis, and system improvement
Collaborate with other AI Engineers, AI Strategists, and other Distillers to make pragmatic system design decisions that balance speed, robustness, maintainability, and customer impact
Take accountability for the production outcomes of the components, workflows, and systems you build
5+ years of software engineering experience
Pragmatic delivery mindset. You can navigate ambiguity, make progress with incomplete information, and balance speed with robustness when building systems that need to work for production users
Willingness to travel. Travel is typically 10–30%, depending on the project, customer needs, and your role on the engagement

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aipython

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Wellness