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 deployed in highly regulated industries. You will own the inference and model-serving infrastructure end to end, making production AI agents fast, reliable, and scalable as concurrency grows. WHAT YOU'LL DO - Design, build, and own inference and model-serving infrastructure from initial architecture through production deployment. - Scale systems that enable AI agents to run reliably and efficiently under increasing concurrent load. - Identify and resolve infrastructure bottlenecks in collaboration with ML and platform engineering teams. - Drive performance optimization across latency, throughput, and reliability for production workloads. WHAT WE'RE LOOKING FOR - 5+ years building and operating ML inference systems, model-serving platforms, or ML infrastructure in production environments. - Hands-on experience designing and scaling inference-serving systems using frameworks such as TensorFlow Serving, TorchServe, Triton, KServe, or equivalent custom solutions. - Strong distributed systems fundamentals, including experience managing concurrent requests and resource allocation under load. - Proficiency with containerization and orchestration technologies, particularly Docker and Kubernetes, for ML workloads. - Experience with cloud infrastructure platforms (AWS, GCP, or Azure) for deploying and managing ML systems. - Solid monitoring and observability skills using tools such as Prometheus, Grafana, ELK, or distributed tracing solutions. - Proficiency in at least one systems or backend language: Python, Go, Rust, C++, or Java. - Familiarity with knowledge graphs, semantic search, or graph databases is a plus. - Background in agentic or autonomous AI systems, real-time inference, or enterprise data infrastructure is a plus. LOCATION On-site in San Mateo, California, United States. Visa sponsorship is not available for this role.