Phylo
Member of Technical Staff - Agent Engineer
South San Francisco · Staff+
Sponsorship not specifiedDetected 10 hours ago
KubernetesMachine LearningData AnalysisLLMsAgentic AIStatisticsSystems EngineeringBioinformaticsResearchCommunication
About the role
- 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.
- 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.
Responsibilities
- Phylo is an applied research lab building agentic intelligence to accelerate discovery for every biomedical scientist.
- Our team brings together engineers, AI researchers, and scientists to build systems capable of carrying out complex scientific work.
- You will own these systems in production and build evaluations to measure whether changes improve performance.
- Build rigorous evaluations that measure agent quality, reliability, latency, and cost on representative scientific tasks.
- 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
- 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.
- 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.
- 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.
Nice to have
- Experience with LLM evaluations, human evaluation, model judges, replay testing, benchmark design, or experiment tracking.
- Experience with task queues, event streams, Kubernetes, code sandboxes, or durable workflow systems.
- Lunch and snacks when you're in the office
- Regular team offsites and company events
Compensation
- Competitive salary and equity share in building the future of biomedical discovery
Company info
- 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.
This listing is sourced directly from Phylo's careers page and normalized into a canonical job model.