MLOps Engineer - Remote-First Equity

The climate crisis is the defining challenge of our time—but it’s also the greatest opportunity for innovation, and a challenge we’re proud to take on. At Overstory, we’re harnessing cutting-edge technology to enable a resilient electrical grid that keeps communities thriving as our world changes.

The grid is the backbone of life as we know it. It powers hospitals, keeps food fresh, and ensures communities stay connected. But extreme weather, aging infrastructure, and growing wildfire risks are putting this critical system under pressure. All of this combined makes the electric utility industry the greatest opportunity for tackling climate change.

One of the leading causes of catastrophic wildfires and power outages? Trees and brush coming into contact with power lines.

That’s where we help. At Overstory, we use AI and advanced satellite imagery to pinpoint and prioritize vegetation risks before they materialize. By giving utilities critical analysis on those risks, we’re helping prevent outages, reduce wildfire risks, and accelerate the transition to a safer, more resilient grid.

Our team spans the Americas and Europe, and we work with utility partners across the Americas and beyond. We’re outdoor enthusiasts, musicians, artists, athletes, parents, and adventurers—15 nationalities strong and growing. What unites us is a passion for solving complex problems, a commitment to climate action, and the belief that technology should be a force for good.

Join us to help us build a more resilient world together.

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

• 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 Vertex
  
  AI 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.

• Experience in remote-first or globally distributed teams.
• Background in image processing, geospatial, or spatio-temporal data processing.
• Prior work on real-time prediction systems or active-learning loops.
• Knowledge of regulatory, privacy, or security considerations in ML.
• Experience optimizing cloud…