Lead Machine Learning / MLOps Engineer

Location: San Jose; USAWork type: HybridSeniority: LeadPrimary skill: Data DevOpsPosted: 2026-10-28We are seeking an experienced and driven Machine Learning / MLOps Engineer to join our engineering team. In this role, you will bridge the gap between machine learning research and production systems. You will be responsible for developing, fine-tuning, deploying, and scaling state-of-the-art machine learning and foundation models in production. If you are passionate about building robust MLOps infrastructure, optimizing multi-GPU workloads, and collaborating with cross-functional teams to deliver high-impact AI solutions, we want to hear from you.Req# 1122672674ResponsibilitiesModel Development & Production: Develop, fine-tune, and deploy machine learning and foundation models into robust production environmentsMLOps & Pipeline Engineering: Build, scale, and operate end-to-end MLOps pipelines, including CI/CD for ML, model lifecycle management, monitoring, and observabilityCloud & Infrastructure Management: Deploy and manage ML workloads on Azure and Kubernetes (AKS), ensuring high availability, scalability, and securityPerformance Optimization: Manage multi-GPU-based workloads and optimize model performance, latency, and resource utilizationSoftware Engineering Excellence: Uphold rigorous software engineering standards, including automated testing, high code quality, CI/CD pipelines, and infrastructure as codeCross-Functional Collaboration: Partner closely with ML researchers, data scientists, and engineering teams to translate research breakthroughs into production-ready systemsRequirementsProduction ML Experience: Hands-on experience developing, fine-tuning, and deploying ML and foundation models in production environmentsProgramming & Frameworks: Strong Python programming skills with deep expertise in PyTorch and/or TensorFlowMLOps Expertise: Proven experience building and operating MLOps pipelines (CI/CD, model tracking, registry, monitoring)Cloud & Containerization: Experience deploying ML workloads on Azure and Kubernetes (AKS)Software Engineering: Strong background in software engineering best practices (unit/integration testing, version control, code quality, automation)Hardware & Performance: Experience managing multi-GPU-based workloads and optimizing inference/training performanceCommunication & Teamwork: Ability to work effectively and collaboratively with ML researchers, data scientists, and cross-functional engineering teamsWe offerMedical, Dental and Vision Insurance (Subsidized)Health Savings AccountFlexible Spending Accounts (Healthcare, Dependent Care, Commuter)Short-Term and Long-Term Disability (Company Provided)Life and AD&D Insurance (Company Provided)Employee Assistance ProgramUnlimited access to LinkedIn learning solutionsMatched 401(k) Retirement Savings PlanPaid Time OffLegal Plan and Identity Theft ProtectionAccident InsuranceEmployee DiscountsPet InsuranceEmployee Stock Purchase Program