Machine Learning Engineer, Operations
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Job Description
Machine Learning Engineer at OptiTrack focusing on designing and automating MLOps systems for advanced motion capture technology and research.
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
Benefits:
- 75% employer-paid medical for employee. Family coverage also included.
- 100% employer paid dental, and vision for employee and dependents
- 100% employer paid long-term, short-term disability, and life insurance policy
- 401k Match, if you’re contributing 5% we match 4%. 100% vested immediately.
- 10 paid holidays
- Starting at 15 days paid PTO (inclusive of sick and vacation time) annually
- Employee Assistance Program (EAP)
- Flexible Spending Account (FSA)
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