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 Responsibilities Design and implement robust MLOps pipelines for data, training, deployment, and monitoring Architect and manage scalable ML infrastructure for distributed training and inference Collaborate with ML engineers to define data, development, and deployment strategies Build cloud-based systems (AWS, GCP) optimized for ML workloads in both R&D and production Enable CI/CD, experiment management, and governance for models and datasets Automate model evaluation, selection, and deployment workflows Key requirements 5+ years building large-scale, reliable systems 2+ years focused on ML infrastructure or MLOps Production-grade ML pipelines and platforms experience Strong knowledge of ML lifecycle (data ingestion, training, evaluation, deployment)Hands-on with MLOps tools (MLflow, Kubeflow, Sage Maker, Airflow, Metaflow)Deep understanding of CI/CD for MLProficiency in Python, Git, and system design Cloud platform experience (AWS, GCP, Azure)collaborationcommunicationproblem-solvingMLflow Kubeflow Sage Maker