Research Engineers, Agents

Distyl AI

$150K — $250K *
Enterprise Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience building agentic AI systems
  • Proficient in Python with strong debugging skills
  • Excellent systems-level reasoning capabilities
  • Research-oriented with a focus on building and testing AI architectures
  • Comfortable using AI tools in a daily workflow
  • Ability to translate business needs into technical designs
  • Strong ownership mentality for production systems

Responsibilities

  • Design and prototype reliable AI systems for complex enterprise workflows
  • Build compound AI architectures with multiple functionalities
  • Investigate agent interactions and recovery methods under real-world conditions
  • Develop frameworks to evaluate agent performance and reliability
  • Create tools to observe, debug, and improve agent behavior
  • Collaborate with AI Researchers and AI Engineers to develop and refine approaches
  • Integrate AI solutions into customer applications and workflows
  • Communicate system capabilities and limitations effectively with stakeholders

Benefits

  • 100% coverage of medical, dental, and vision insurance for employees and dependents
  • 401(k) plan with additional perks like commuter benefits
  • Access to advanced AI models and tools for real-world applications
  • Ownership of significant projects within top enterprises
  • Culture that values curiosity, pragmatism, and excellence
Full Job Description
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% 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
  • A mission-driven, fast-moving culture that prizes 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

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