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Overstory · 2 weeks ago

Staff Machine Learning Operations Engineer

United States

Full-time

Remote

Senior Level, Lead/Staff

5+ years exp

Overstory is tackling the climate crisis by leveraging innovative technology to enhance the resilience of electrical grids. As a Staff Machine Learning Operations Engineer, you will design and build the foundations of machine learning operations, ensuring model reliability and delivering value to customers through collaboration with data engineers and scientists.

AnalyticsArtificial Intelligence (AI)Big DataMachine LearningSoftware

Responsibilities

Design, build, and maintain processes and systems such as:

Automated pipelines for training, testing, and deploying ML models

Experiment tracking systems for performance metrics, data and model versioning, and documentation

Processes and systems for the full model lifecycle, including registries, release and rollback strategies, and scalable model serving

Monitoring and alerting for prediction quality, system health, and cost optimization

Advocating for a balance between MLOps best practices and quick slices of value

Aligning technical solutions with customer needs in collaborating with both engineering and product

Ensuring our MLOps systems support regulatory, privacy, and security requirements

Qualification

Machine Learning OperationsProduction-grade ML pipelinesExperiment tracking systemsML infrastructure toolsGCPVertexAIArchitectural decision makingBackground in image processingRegulatory considerationsOptimizing cloud infrastructure costsCommunication skillsCollaborationAdaptability

Required

10+ years of experience with designing and building production-grade ML pipelines and systems – but don't filter yourself out if you feel you're a strong candidate with 5+ years

Strong knowledge of experiment tracking, model deployment strategies, data versioning, and monitoring

Experience with ML infrastructure tools (e.g. MLflow, Kubeflow, Airflow, feature stores, model registries)

Strong communication skills and ability to align technical solutions with business goals

Comfortable making architectural decisions and balancing best practices with practical trade-offs