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 At Distyl, Research Engineers build the bridge between frontier AI research and production systems that deliver real business value. This role is for engineers who are excited to investigate how AI systems should be designed, rapidly prototype new ideas, and turn promising concepts into reliable systems that work inside real customer environments. Research Engineers operate at the intersection of applied research, systems engineering, and customer-facing deployment. They design and implement compound AI systems, run experiments to understand system behavior, build evaluation frameworks, and collaborate closely with AI Researchers, AI Engineers, and customer stakeholders. Their work is not limited to demos or isolated prototypes: they help turn new techniques into robust systems that can be measured, operated, and improved in production. KEY RESPONSIBILITIES - Design, prototype, and implement agentic AI systems that perform reliably across complex enterprise workflows - Build compound AI architectures that combine planning, tool use, retrieval, memory, evaluation, orchestration, and execution - Investigate how agents reason, coordinate, recover from errors, and interact with external systems under real-world constraints - Develop evaluation frameworks that measure agent behavior, task completion, reliability, robustness, and failure modes - Create tools and abstractions that make agent behavior easier to observe, debug, test, and improve - Partner with AI Researchers to explore new agent architectures and with AI Engineers to harden successful approaches for production use - Integrate agents into customer APIs, applications, data platforms, and operational workflows - Communicate clearly with internal teams and customer stakeholders about agent capabilities, limitations, tradeoffs, and risks WHO YOU ARE - Experience Building Agentic Systems: You have built AI systems that use models, tools, retrieval, planning, memory, or multi-step execution to complete real tasks - Strong Engineering Fundamentals: You write clean, maintainable Python and are comfortable debugging complex, stateful systems - Systems-Level Reasoning: You think holistically about how prompts, tools, context, evaluators, state, orchestration, and external APIs interact - Research-Oriented Builder: You are curious about why agents succeed or fail, and you can design experiments to test different architectures and behaviors - AI-Native Working Style: You use AI tools daily to write code, debug systems, explore designs, analyze traces, and accelerate experimentation - Bias Towards Showing vs. Telling: You prefer working demonstrations, traces, evaluations, and production behavior over abstract descriptions - Comfort in Customer Environments: You can translate ambiguous business workflows into concrete agent designs and explain system behavior clearly to stakeholders - Ownership Mentality: You take responsibility for whether an agentic system performs reliably, safely, and usefully in production WHAT WE OFFER - The base salary range for this role is $150K – $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

Design, prototype, and implement agentic AI systems that perform reliably across complex enterprise workflows
Build compound AI architectures that combine planning, tool use, retrieval, memory, evaluation, orchestration, and execution
Investigate how agents reason, coordinate, recover from errors, and interact with external systems under real-world constraints
Develop evaluation frameworks that measure agent behavior, task completion, reliability, robustness, and failure modes
Create tools and abstractions that make agent behavior easier to observe, debug, test, and improve
Partner with AI Researchers to explore new agent architectures and with AI Engineers to harden successful approaches for production use
Integrate agents into customer APIs, applications, data platforms, and operational workflows
Communicate clearly with internal teams and customer stakeholders about agent capabilities, limitations, tradeoffs, and risks
Experience Building Agentic Systems: You have built AI systems that use models, tools, retrieval, planning, memory, or multi-step execution to complete real tasks
Strong Engineering Fundamentals: You write clean, maintainable Python and are comfortable debugging complex, stateful systems
Systems-Level Reasoning: You think holistically about how prompts, tools, context, evaluators, state, orchestration, and external APIs interact
Research-Oriented Builder: You are curious about why agents succeed or fail, and you can design experiments to test different architectures and behaviors
AI-Native Working Style: You use AI tools daily to write code, debug systems, explore designs, analyze traces, and accelerate experimentation
Bias Towards Showing vs. Telling: You prefer working demonstrations, traces, evaluations, and production behavior over abstract descriptions
Comfort in Customer Environments: You can translate ambiguous business workflows into concrete agent designs and explain system behavior clearly to stakeholders
Ownership Mentality: You take responsibility for whether an agentic system performs reliably, safely, and usefully in production

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