About the role
Member of Technical Staff – Agent Engineer About Phylo Phylo is an applied research lab building agentic intelligence to accelerate discovery for every biomedical scientist. We believe AI agents will fundamentally transform how biomedical research is done. Our team brings together engineers, AI researchers, and scientists to build systems capable of carrying out complex scientific work.
About
the role We’re looking for an engineer with research or production experience in AI agents to build and evaluate systems that make our agents capable, reliable, and measurably better. The model is only one part of an agent; the harness shapes how it plans, uses tools, manages context, coordinates work, and recovers from failure. You will own these systems in production and build evaluations to measure whether changes improve performance. The work includes bringing the latest advances and original ideas into production. The ideal candidate combines strong engineering execution with a quantitative mindset and an interest in AI agents for scientific discovery. What you’ll work on Advance the agent harness by bringing the latest research and open-source developments into production and experimenting with new approaches to multi-agent coordination, model routing, memory, planning, and tool use. Build rigorous evaluations that measure agent quality, reliability, latency, and cost on representative scientific tasks. Monitor agent quality in production, troubleshoot failures quickly in high-ambiguity situations, and translate findings into engineering improvements. Collaborate on reliable infrastructure for long-running sessions, safe sandboxed execution, background work, and subagents. Partner with scientists and engineers to evaluate and productionize new agent capabilities.
Requirements Strong software engineering experience building production backend systems, infrastructure, or distributed systems. Research or production experience in machine learning, or another quantitative discipline. Familiarity with LLM APIs, tool calling, agent runtimes, or workflow orchestration. Strong quantitative judgment and the ability to determine whether an apparent improvement is real, reproducible, and meaningful. Ability to move between research questions, data analysis, system design, and production implementation. Experience or strong interest in AI for science, scientific agents, computational research, or automated scientific discovery. Experience working in AI-native teams that use coding agents or automation extensively. High ownership, clear communication, and strong engineering judgment. We care more about demonstrated ability than a particular credential. Relevant backgrounds may include ML or LLM engineering, academic research paired with substantial software development, scientific computing, or production systems engineering with demonstrated quantitative experience.