Staff Machine Learning Ops Engineer, Overstory

• You love working in a remote-first, fast-moving environment where collaboration and adaptability are essential
• 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)
• Familiarity with GCP and VertexAI preferred, but not required
• Strong communication skills and ability to align technical solutions with business goals
• Comfortable making architectural decisions and balancing best practices with practical trade-offs

Desirable

• As a Staff Machine Learning Ops Engineer at Overstory, you will design and build the foundations of our machine learning operations, ensuring our models are reliable, maintainable, and deliver real value to customers
• You’ll help architect end-to-end systems for experiment tracking, data management, and scalable deployment
• As one of our first dedicated MLOps hires, you’ll have significant ownership and influence over our technical direction, balancing best practices with pragmatic delivery to help our teams move fast while maintaining trust and reliability in production
• You’ll also collaborate closely with data engineers, data scientists, and machine learning engineers, as well as future MLOps colleagues
• In collaboration with your data and ML colleagues, you will 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
• You will also influence the direction of data and ML within Overstory by:
• 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