Research Engineers at Distyl build and productionize reliable agentic AI systems and compound architectures for enterprise workflows. They design agents, develop evaluation frameworks, run experiments on reasoning and failure modes, and integrate into customer environments.
150k – 250k
HybridML Engineering
About the role
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
Requirements
Experience building agentic AI systems that use models, tools, retrieval, planning, memory, or multi-step execution to complete real tasks
Strong engineering fundamentals: clean, maintainable Python and debugging complex, stateful systems
Systems-level reasoning about how prompts, tools, context, evaluators, state, orchestration, and external APIs interact
Research-oriented builder: curious about why agents succeed or fail; design experiments to test architectures and behaviors
AI-native working style: use AI tools daily to write code, debug, explore designs, analyze traces, and accelerate experimentation
Bias towards showing vs. telling: prefer working demonstrations, traces, evaluations, and production behavior
Comfort in customer environments: translate ambiguous business workflows into concrete agent designs and explain system behavior to stakeholders
Ownership mentality: responsibility for whether an agentic system performs reliably, safely, and usefully in production
Compensation and Benefits
Base salary range: $150000 – $250000, depending on experience, location, and level
Meaningful equity
100% covered medical, dental, and vision for employees and dependents
401(k) with additional perks (e.g., commuter benefits, in‑office lunch)
Access to state‑of‑the‑art models, generous usage of modern AI tools, and real‑world business problems
Ownership of high‑impact projects across top enterprises
Mission‑driven, fast‑moving culture that prizes curiosity, pragmatism, and excellence
Skills
PythonAgentic Ai SystemsCompound Ai ArchitecturesPlanningTool UseRetrievalMemoryEvaluation FrameworksOrchestrationMulti-Step ExecutionPrompt EngineeringDebuggingExperiment Design
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