OptiTrack
Tech / AI / Software
MachineLearning&OperationsEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Machine Learning & Operations Engineer at OptiTrack. Skills: MLOps, ML infrastructure, Machine Learning, Python, CI/CD, Cloud Platforms, Distributed Systems. Design and maintain automated ML training pipelines. Build infrastructure for large-scale distributed experimentation”
What You'll Achieve.
scale an MLOps system; provide other support to teams working on projects involving machine learning; automation of data validation pipelines; orchestration of large-scale experiments; deployment of high-performance algorithms; make new algorithms product ready
Industry & Context.
problem-solving skills
Ability to work with both European and US developers
What They're Looking For.
Must Have
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), understanding of automation, infrastructure reliability, and data pipelines, Ability to work with both European and US developers
Nice to Have
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, 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
What You'll Do.
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
Implement experiment tracking
hyperparameter tuning automation
and reproducibility systems
Optimize GPU/compute utilization across cloud and on-prem environments
and maintain production ML models
Establish and enforce MLOps best practices including model registry
Improve system reliability
Collaborate closely with ML researchers make new algorithms product ready
More typical DevOps responsibilities for software development as required
How You'll Work.
Team & Collaboration
working cross-functionally with research and engineering teams; Collaborate closely with ML researchers; Comfortable working in a remote, collaborative environment, with international team members; Clear communicator who can bridge research and production engineering
Communication Scope
Clear communicator who can bridge research and production engineering
Full Job Description
OptiTrack is a global leader in motion capture technology, delivering precision tracking solutions for animation, robotics, virtual production, biomechanics, and industrial applications. About the Role OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale an MLOps system and provide other support to teams working on projects involving machine learning. This role sits at the intersection of machine learning engineering and infrastructure, focusing on automation of data validation pipelines, orchestration of large-scale experiments, and deployment of high-performance algorithms. This is a fully remote position, working cross-functionally with research and engineering teams. What You’ll Do * 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** Required Qualifications * 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 *
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