ML Infrastructure Engineer

Job description

### About the Role This is a hands-on ML Infrastructure Engineer role at an early-stage enterprise AI company building a context and data governance layer that makes AI agents reliable in production. You will own the inference and model-serving infrastructure end to end, ensuring agents run fast and reliably at increasing concurrency. The work is squarely production-focused with real-world impact across regulated industries like insurance, banking, healthcare, and asset management. ### What You'll Do - Design, build, and scale inference and model-serving infrastructure from the ground up through production deployment. - Optimize systems for latency, throughput, and reliability under high concurrency. - Collaborate closely with ML and infrastructure teams to ensure seamless integration and surface performance bottlenecks. - Drive solutions to infrastructure challenges across a fast-moving, cross-functional team. ### 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 systems using tools such as TensorFlow Serving, TorchServe, Triton, KServe, or equivalent custom solutions. - Strong distributed systems fundamentals, including containerization and orchestration with Docker and Kubernetes. - Proficiency with monitoring and observability tooling for production systems, such as Prometheus, Grafana, or distributed tracing frameworks. - Experience deploying and managing ML workloads on cloud platforms (AWS, GCP, or Azure). - Proficiency in at least one systems or backend language: Python, Go, Rust, C++, or Java. - Comfort collaborating across both ML and infrastructure disciplines in a fast-paced environment. - **Nice to have:** experience with knowledge graphs, semantic search, or graph databases; real-time or low-latency inference systems; agentic or multi-step AI pipelines; enterprise data integration or pipeline infrastructure. ### Location On-site in San Mateo, California, United States. Visa sponsorship is not available for this role.