ML Infrastructure Engineer

About the Role

This is a hands-on infrastructure engineering role at an early-stage enterprise AI company building a context and data governance layer for AI agents in highly regulated industries. You will own the inference and model-serving infrastructure end to end, ensuring AI agents run reliably, accurately, and at scale in production environments where performance is non-negotiable.

What You'll Do

• Design, build, and operate inference and model-serving infrastructure from development through production deployment.

• Scale systems to support AI agents running reliably under increasing concurrency and production load.

• Identify and resolve infrastructure bottlenecks in close collaboration with ML and platform engineering teams.

• Optimize systems for latency, throughput, and reliability at scale.

What We're Looking For

• 5 or more years building and operating machine learning inference systems, model-serving platforms, or ML infrastructure in production environments.

• Hands-on experience designing and scaling inference serving infrastructure using tools such as TensorFlow Serving, TorchServe, Triton, KServe, or equivalent custom systems.

• Strong systems engineering fundamentals with expertise in distributed systems, containerization, and orchestration (Docker, Kubernetes).

• Demonstrated ability to optimize production ML systems for latency, throughput, and reliability under high concurrency.

• Experience with cloud infrastructure platforms such as AWS, GCP, or Azure for deploying and managing ML workloads.

• Proficiency with monitoring, observability, and debugging tools such as Prometheus, Grafana, ELK, or distributed tracing frameworks.

• Proficiency in at least one systems programming or backend language: Python, Go, Rust, C++, or Java.

• Experience with knowledge graphs, semantic search, or graph databases (e.g., Neo4j, Amazon Neptune) is a plus.

• Familiarity with agentic AI systems, autonomous agents, or multi-step reasoning pipelines is a plus.

• Experience with enterprise data infrastructure, data pipelines, or data integration platforms is a plus.

Location

This role is on-site in San Mateo, California. Visa sponsorship is not available.