Staff ML Ops Engineer

Required Skills & Experience

• Strong experience with cloud platforms: AWS, GCP, and/or Azure
• Hands-on experience operating Kubernetes or managed Kubernetes services in production
• Experience building or maintaining MLOps platforms supporting training and inference
• Familiarity with ML experiment tracking and orchestration tools (e.g., MLflow, Weights & Biases, Slurm, Ray, Kubeflow, or similar)
• Experience deploying ML models into production-facing applications or services
• Strong understanding of CI/CD, infrastructure-as-code, and automation
• Proficiency in Python; experience with Bash or another scripting language
• Ability to collaborate effectively across research and engineering teams

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