MLOps Engineer

Nice to Have Skills & Experience Experience working with robotics or real-time telemetry data Familiarity with streaming data systems (e.g., Kafka, Pub/Sub, Kinesis) Experience supporting GPU workloads in cloud or Kubernetes environments Exposure to edge–cloud ML deployment or fleet-based systems Prior work in robotics, autonomy, or embodied AI environments Job Description We are seeking a Cloud MLOps Engineer to build and operate the cloud infrastructure that powers machine learning for a 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 real‑world robotic systems #J-18808-Ljbffr