> Markdown version of [/jobs/ext/4629307-ml-infrastructure-engineer](https://www.wearedevelopers.com/jobs/ext/4629307-ml-infrastructure-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Infrastructure Engineer - **Company:** Strativ Group - **Location:** Menlo Park, CA, United States - **Experience:** Expert - **Salary:** $250,000.0 - $320,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Distributed Systems, Python (Programming Language), Software Construction, System Availability, Kubernetes, Information Technology, Machine Learning Operations, Terraform, Data Pipelines, Docker - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25304_a80c048160ff464910c604983f857b6e ## About the Role * 5+ years of experience in Software / ML Infrastructure Engineering. * Deep experience with distributed systems and GPU orchestration for high-performance ML workloads. * Proficiency in Python, Go, or similar, and strong grasp of software engineering best practices. * Hands-on expertise with Kubernetes, Docker, and IaC (Terraform). * Experience optimizing model serving and data pipelines for latency and scalability. * A builder's mindset - you thrive in ambiguity, pick the right tools for the job, and ship. Seniority level: Mid-Senior level. Employment type: Full-time. Job function: Information Technology. ## Description As a Staff Infra Engineer, you will work directly with the founders to architect, build, and scale the compute substrate that powers this next generation of AI. You'll design and optimize the inference platform, GPU-based training clusters, and data processing pipelines that drive real-time creativity and discovery. You'll play a key role in scaling systems for both research and production-ensuring low-latency performance, high availability, and efficient utilization across petabyte-scale data and model-serving workloads.