Partner with ML Scientists, Data Scientists, and Perception Engineers to translate research code into dependable, scalable production services without slowing research velocity.
Coordinate with DevOps and Software Engineering on infrastructure requests and data pipeline needs; support automation initiatives and team goals.
Maintain and improve the model registry and deployment pipelines; implement safer release practices (e.g., shadow deployments, rollbacks).
Build and troubleshoot cloud infrastructure and CI/CD pipelines for ML workloads; collaborate on IaC and cost optimization.
Implement monitoring and observability for models and pipelines; track performance and drift; support experiment tracking and versioning.
Keep deployed ML systems healthy with upgrades, retraining support, and extended platform support.
Define conventions for model versioning, deployment promotion, and model documentation/ lineage; build self-serve tooling.
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or related field; typically 4+ years in MLOps, ML platform engineering, or infrastructure supporting ML.
Strong Python and solid software engineering fundamentals (testing, code review, version control).
Hands-on experience with AWS (or major cloud), Terraform, GitHub Actions, Docker, Kubernetes.
Experience building/operating production ML pipelines and model registries, including versioning a

About the role

Qualifications:

Partner with ML Scientists, Data Scientists, and Perception Engineers to translate research code into dependable, scalable production services without slowing research velocity.
Coordinate with DevOps and Software Engineering on infrastructure requests and data pipeline needs; support automation initiatives and team goals.
Maintain and improve the model registry and deployment pipelines; implement safer release practices (e.g., shadow deployments, rollbacks).
Build and troubleshoot cloud infrastructure and CI/CD pipelines for ML workloads; collaborate on IaC and cost optimization.
Implement monitoring and observability for models and pipelines; track performance and drift; support experiment tracking and versioning.
Keep deployed ML systems healthy with upgrades, retraining support, and extended platform support.
Define conventions for model versioning, deployment promotion, and model documentation/ lineage; build self-serve tooling.
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or related field; typically 4+ years in MLOps, ML platform engineering, or infrastructure supporting ML.
Strong Python and solid software engineering fundamentals (testing, code review, version control).
Hands-on experience with AWS (or major cloud), Terraform, GitHub Actions, Docker, Kubernetes.
Experience building/operating production ML pipelines and model registries, including versioning a

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