ML Platform Engineer (m/f/d)

About this role

The AI Teams at Agile Robots are looking for an ML Platform Engineer (m/f/d) , who will build and operate the distributed training, deployment, and experimentation infrastructure that research, data, and robotics teams depend on to move models from prototype to production. Your Responsibilities Training Infrastructure: Design and scale distributed training workflows for large models using tools such as PyTorch Distributed, DeepSpeed, and cluster schedulers like SLURM or Kubernetes.

ML Platform: Build and maintain containerised ML environments that support reproducible experimentation and benchmarking. CI/CD Pipelines: Develop and maintain CI/CD pipelines for machine learning systems to enable reliable testing, training, and deployment of models. Lifecycle Management: Implement experiment tracking, model versioning, and reproducibility workflows using tools such as ClearML or Weights & Biases. Observability: Set up monitoring systems such as Prometheus and Grafana to track model performance and system health and detect drift in production.

Cross-Team Collaboration: Work with research, data, and robotics teams to connect new models to robust production systems. Essential Skills Background and Experience: Degree in Computer Science, Software Engineering, or a related field, with professional experience building and operating ML or software infrastructure in production. Distributed Training: Experience designing and operating distributed training systems on Kubernetes and Docker, using PyTorch Distributed, DeepSpeed, and schedulers such as SLURM.

CI/CD for ML: Experience building CI/CD pipelines that support reliable model testing, training, and deployment. Cloud Infrastructure: Experience operating ML workloads on cloud infrastructure, preferably AWS. Experiment Tracking: Hands-on experience with experiment tracking and model versioning using tools such as MLflow or Weights & Biases. Observability: Experience with monitoring and drift detection using tools such as Prometheus and Grafana.

Software Engineering: Python and system design skills, with experience building and operating ML systems beyond the prototype stage. Beneficial Skills Multimodal Systems: Experience with large-scale or multimodal ML systems such as vision-language-action models. Infrastructure As Code: Familiarity with infrastructure-as-code tools such as Terraform. ML Orchestration: Experience with ML pipeline and orchestration tools.