Software Engineer, Agent Platform

Responsibilities

• Own and evolve the internal LLM agent framework, including core abstractions, runtime architecture, developer experience, integrations, and reliability.
• Support teams building LLM agents through framework capabilities, implementation guidance, architectural reviews, and best-practice patterns.
• Integrate supervised fine-tuning, offline reinforcement learning, online reinforcement learning, and environment-driven agent improvement workflows.
• Build tooling for structured tool calling, filesystem-using agents, memory and retrieval, planning loops, subagents, agent graphs, and human-in-the-loop workflows.
• Define evaluation suites covering task success, tool-call correctness, trajectory quality, robustness, regressions, and deployment readiness.
• Make pragmatic decisions about emerging agent architecture and evaluation techniques for high-stakes LLM applications.

Requirements

• Strong backend engineering experience building production-quality platforms, frameworks, APIs, or infrastructure used by other engineers.
• Deep expertise in LLM agent framework design and orchestration patterns, including linear agents, graph-based agents, multi-agent systems, planner/executor loops, and tool-heavy agents.
• Experience designing agent evaluation paradigms such as trajectory evaluations, LLM-as-judge workflows, task-success metrics, tool-call correctness checks, rubric-based grading, adversarial testing, regression suites, and human review.
• Familiarity with model post-training workflows including supervised fine-tuning, preference tuning, reinforcement learning, and environment-based agent training.
• Strong judgment around reliability, observability, debugging, and safety for LLM applications in high-stakes settings.
• Ability to work with partner teams, understand ambiguous product needs, and turn them into reusable platform capabilities.
• Preferred experience with LangChain DeepAgents, Claude SDK, evaluation platforms, simulation environments, benchmark suites, agent test harnesses, Kubernetes, Docker, distributed systems, workflow orchestration, or ML infrastructure.
• Familiarity with defense, robotics, command-and-control systems, autonomy, or operational planning domains is preferred.

Benefits

• Full-time employees receive comprehensive competitive benefits available at little to no cost.
• Highly competitive equity grants are included in the majority of full-time offers.
• The role’s stated base salary range is $220,000—$292,000 USD.