Research Engineers, Agents

Distyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. 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.

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

Requirements

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

Benefits

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