Job Description
We are seeking a Cloud MLOps Engineer to build and operate the cloud infrastructure that powers machine learning for a humanoid robotics platform. This role sits at the intersection of ML research, production systems, and end-user applications, with a strong focus on robot telemetry data, model lifecycle management, and production deployment. You will enable researchers and applied ML engineers to reliably train, evaluate, and deploy models at scale, while ensuring telemetry-driven insights flow from robots in the real world back into continuous learning systems. What You'll Do Design, deploy, and maintain cloud-native MLOps platforms supporting large-scale ML training, evaluation, and inference workloads Operate Kubernetes-based infrastructure (self-managed or managed services such as GKE, EKS, or AKS) for ML workloads and data applications Build and maintain end-to-end ML pipelines that bridge research workflows with production systems Support robot telemetry ingestion, processing, and analytics, enabling model feedback loops from deployed humanoid robots Integrate and operate ML tooling such as MLflow, Weights & Biases, Slurm, or similar systems for experiment tracking, scheduling, and reproducibility Enable model deployment to production, including CI/CD for models, versioning, monitoring, and rollback strategies Partner closely with ML researchers, perception, controls, and applications teams to productionize models safely and efficiently Implement observability across ML systems, including model performance, data drift, and system health Improve reliability, scalability, and security of cloud ML infrastructure supporting realworld robotic systems We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will r