• Design and implement an ML platform orchestrating data processing, training, evaluation, and deployment workflows.
• Develop reliable workflows across cloud compute, Kubernetes, and continuous automation.
• Build model registry, feature store, experiment tracking, and related ML infrastructure components.
• Own developer-facing APIs and CLI tools that make ML workflows simple and reproducible.
• Implement continuous integration and delivery for ML, including continuous retraining, automated testing, and model delivery to production.
• Integrate ML orchestration and metadata tracking tools with existing data lakes and pipelines.
• Apply MLOps practices including reproducibility, lineage, rollback, monitoring, and governance.
• Collaborate with AI researchers, robotics engineers, data engineers, and platform teams to deliver scalable systems.
• Mentor junior engineers and influence the broader cloud platform organization’s roadmap.

Requirements

• 5+ years of software engineering experience, including at least 2+ years working on ML infrastructure, data platforms, or production MLOps systems.
• Experience building and maintaining ML platform components such as experiment tracking, model registries, training pipelines, or deployment systems.
• Familiarity with MLflow, WandB, Airflow, Kubeflow, or similar orchestration and tracking tools.
• Proficiency with AWS, GCP, or Azure, containers, and infrastructure-as-code tools such as CDK or Terraform.
• Hands-on experience processing or modeling multimodal data such as sensor logs, camera streams, and behavior traces.
• Ability to collaborate with research scientists, data engineers, and robotics or autonomy teams.
• Experience with robotics, autonomous vehicles, drones, or embedded ML is a bonus.
• Contributions to open-source ML infrastructure or MLOps tooling are a bonus.