Senior ML Ops Engineer (Machine Learning Infrastructure)

Overview In this role you will lead the design and development of scalable ML infrastructure powering our autonomy and perception pipelines. You will own the ML engineering stack—from data handling and model training to deployment and monitoring—across R&D and production. You’ll collaborate with autonomy, robotics, and software teams to enable safe, real-time ML capable systems. This is a chance to build robust platforms that accelerate AI and robotics innovation at scale. You’ll drive end-to-end ML workflows with measurable impact. Compensation / Benefitshybrid work arrangementequal opportunity employerreasonable accommodationscompetitive compensationopportunity to work with autonomous systemsgrowth and impact in robotics ResponsibilitiesDesign and implement robust MLOps pipelines for data, training, deployment, and monitoringArchitect and manage scalable ML infrastructure for distributed training and inferenceCollaborate with ML engineers to define data, development, and deployment strategiesBuild cloud-based systems (AWS, GCP) optimized for ML workloads in both R&D and productionEnable CI/CD, experiment management, and governance for models and datasetsAutomate model evaluation, selection, and deployment workflows Key requirements5+ years building large-scale, reliable systems2+ years focused on ML infrastructure or MLOpsProduction-grade ML pipelines and platforms experienceStrong knowledge of ML lifecycle (data ingestion, training, evaluation, deployment)Hands-on with MLOps tools (MLflow, Kubeflow, SageMaker, Airflow, Metaflow)Deep understanding of CI/CD for MLProficiency in Python, Git, and system designCloud platform experience (AWS, GCP, Azure)collaborationcommunicationproblem-solvingMLflowKubeflowSageMaker