Staff Machine Learning Engineer

Join our team as a Staff Machine Learning Engineer, where you will play a crucial role in building and maintaining the infrastructure that enables AI agents to operate effectively in enterprise environments. You will be responsible for designing and building the agent execution harness, ensuring reliability at scale, and integrating with frontier large language models. Additionally, you will provide technical leadership and define design standards for the team. This position offers a range of benefits, including generous family leave, matched donations, and flexible PTO.

- Design and build the agent execution harness, including the orchestration layer that routes inputs, manages context, invokes tools, handles retries, and surfaces execution state across multi-step workflows. - Own the runtime's fault tolerance, latency, and throughput; design for enterprise workflows that cannot fail silently or unpredictably. - Integrate with and abstract over frontier LLMs, managing model routing, fallback strategies, cost, and latency trade-offs in production.

- Hands-on experience shipping generative AI products — not just integrating LLM APIs or building prototypes, but owning AI-powered features that production users depend on - Comfort operating in fast-moving, ambiguous, startup-like AI product environments - Solid depth in how large language models work: failure modes, context constraints, and how prompt design shapes model behavior at scale - Python, Kubeflow,Experience with SageMaker, Vertex AI or AzureML, SQL - Strong software engineering fundamentals: distributed systems, API design, and testing discipline - Practical prompt engineering experience: systematically designing, versioning, and evaluating prompts across model updates or A/B evaluation cycles - A real track record in eval engineering — not just familiarity, but a portfolio of evaluation suites designed, shipped, and used to drive quality decisions in production AI systems - Cost and efficiency awareness at the system level: experience reasoning about model routing, inference cost, and latency tradeoffs in production - 7+ years building production software systems with a strong track record on reliability, performance, and scalability - Experience with multi-agent coordination patterns (A2A, MCP) - Familiarity with agent frameworks (LangChain, LlamaIndex, or similar) - Prior experience shipping AI systems in enterprise software - Experience with AI observability tooling (tracing, cost tracking, LLM-specific monitoring) - Familiarity with cloud-native infrastructure, service observability, logging, monitoring, reliability engineering, and production troubleshooting