Machine Learning & Operations Engineer
New
O
OptiTrackMotion capture technology
United StatesFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3+ years
- Required Skills
- AWSDockerPythonGCPJenkinsPyTorchAzureTensorflowGitHub ActionsMLOps
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
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.
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