ML Platform Engineer

Job summary

Build and operate a cloud platform that enables customers to run robot-learning training and inference pipelines on their own data as a hosted service. The role spans cloud architecture, Python and PyTorch job services, a web application for uploading data and managing training runs, infrastructure automation, security, and cost control for confidential industrial workloads.

Benefits

Qualifications

• Master’s degree or PhD in computer science or a related field, plus 2 to 3 years of experience building and operating a comparable cloud service.
• Production workload experience with a major cloud provider, such as AWS, GCP, Azure, Scaleway, or OVHcloud.
• Proficiency with Docker and Kubernetes or managed equivalents, and Terraform or similar infrastructure-as-code tools.
• Strong Python backend development skills, including asynchronous job execution.
• Experience with a modern web framework such as React, Vue, or Svelte.
• Experience with CI/CD, monitoring, and security for a multi-tenant service.
• Comfort working in Linux environments with Git.
• Professional proficiency in English.

Responsibilities

• Design the platform architecture and select the cloud provider, orchestration layer, and web stack.
• Package Python and PyTorch training and inference pipelines as cloud services, including job scheduling, GPU provisioning, artifact storage, and dataset and policy versioning.
• Build the web application and API for data uploads, training-run tracking, and policy downloads.
• Develop interactive browser-based platform components, including simulation.
• Set up infrastructure as code, CI/CD, monitoring, logging, and alerting.
• Implement authentication, customer isolation, and protection for confidential industrial data.
• Manage cloud costs, including GPU usage, storage, and idle resources.
• Share cloud and DevOps practices through documentation, reviews, and team training.

Skills

Degrees

Languages

Industry

Relocation