Posted 1w ago

Machine Learning & Operations Engineer

@ OptiTrack
New York or United States
RemoteFull Time
Responsibilities:Design pipelines, Maintain infrastructure, Deploy models
Requirements Summary:3+ years in MLOps/ML infra or related; Python and ML frameworks; CI/CD pipelines; Docker; GPU workloads; cloud platforms; data pipelines; remote collaboration.
Technical Tools Mentioned:Python, PyTorch, TensorFlow, GitHub Actions, GitLab CI, Jenkins, Docker, AWS, GCP, Azure, MLflow, Weights & Biases, Airflow, Argo, Prefect, Kubeflow, Terraform, Pulumi, CloudFormation
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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.