Staff Machine Learning Engineer
Core
Design and build the agent execution harness (orchestration layer) that enables AI agents to reason over enterprise data, manage multi-agent workflows, and execute actions reliably at Fortune 500 scale.
Role type
Staff IC Machine Learning Engineer (Agentic AI Systems)
Builds
Agent harness, orchestration runtime, multi-agent coordination systems, and evaluation frameworks for production AI agents.
Domain
Enterprise AI / Agentic Workflows / Large Language Models
Deliverable
production ML models
Required skills
Generative AI product ownership, LLM failure modes and context constraints, Python, Kubeflow, SageMaker/Vertex AI/AzureML, SQL, Prompt engineering (versioning/evaluation), Eval engineering (unit/integration/production monitors), Distributed systems, API design, Testing discipline
Preferred skills
Multi-agent coordination patterns (A2A, MCP), Agent frameworks (LangChain, LlamaIndex), AI observability tooling, Cloud-native infrastructure reliability engineering
Technologies
Kubeflow, SageMaker, Vertex AI, AzureML, Python, SQL
Responsibilities
Design and build the agent execution harness; Own runtime fault tolerance, latency, and throughput; Instrument harness with tracing and cost attribution; Build prompt management systems; Design evaluation frameworks; Integrate with and abstract over frontier LLMs; Set technical standards and coach on agentic design patterns; Define system boundaries for agent logic.
Seniority
Staff, hands-on IC with technical leadership
