Machine Learning & Operations Engineer

OptiTrack is a global leader in motion capture technology, delivering precision tracking solutions for various applications. They are seeking a Machine Learning Engineer to design, automate, and scale an MLOps system while supporting teams on machine learning projects.

Responsibilities:

• Design and maintain automated ML training pipelines
• Build infrastructure for large-scale distributed experimentation
• Develop CI/CD workflows tailored for machine learning systems
• Orchestrate data ingestion, preprocessing, validation, and model versioning
• Implement experiment tracking, hyperparameter tuning automation, and reproducibility systems
• Optimize GPU/compute utilization across cloud and on-prem environments
• Deploy, monitor, and maintain production ML models
• Establish and enforce MLOps best practices including model registry, artifact management, and observability
• Improve system reliability, performance, and security
• Collaborate closely with ML researchers make new algorithms product ready
• More typical DevOps responsibilities for software development as required

Requirements:

• 3+ years of experience in MLOps, ML infrastructure, Machine Learning, or related roles or relevant degree experience
• Experience with Python and ML frameworks (PyTorch, TensorFlow, or similar)
• Experience building CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, etc.)
• Hands-on experience with containerization (Docker) and orchestration
• Experience managing GPU workloads and distributed training systems
• Experience with cloud platforms (AWS, GCP, or Azure)
• Strong understanding of automation, infrastructure reliability, and data pipelines
• Ability to work with both European and US developers
• Experience with motion capture or computer vision systems
• Familiarity with experiment tracking tools (MLflow, Weights & Biases, etc.)
• Background in distributed systems or high-performance computing
• Experience with workflow orchestration tools (Airflow, Argo, Prefect, Kubeflow)
• Infrastructure as Code experience (Terraform, Pulumi, CloudFormation)
• Experience with model optimization, inference acceleration, or edge deployment
• Experience building tracking algorithms for device localization using techniques like SLAM
• Strong problem-solving skills and attention to reproducibility
• Comfortable working in a remote, collaborative environment, with international team members
• Clear communicator who can bridge research and production engineering
• Passion for building scalable AI infrastructure