Software Engineer - ML Platform​/Sr

Software Engineer - ML Platform (Staff / Sr Staff)

San Francisco or US Remote

About our Company

Equilibrium Energy is revolutionizing the clean energy transition by developing innovative grid-scale energy storage solutions. Our technology and market platform helps utilities, independent power producers, and commercial customers optimize their renewable energy assets, improve grid reliability, and accelerate decarbonization. As a fast-growing climate tech company, we’re building infrastructure that will shape the future of energy markets and enable a sustainable energy economy.

Equilibrium Energy is a well-funded, Series B clean energy startup backed by some of the most prominent institutional investors in climate. New colleagues will share our vision that a next-generation energy company must be built from the ground up on deep industry expertise combined with an unwavering commitment to modern digital approaches. We’re looking for collaborative, talented, passionate and resourceful folks to join our team and help us lay the foundation for our important mission and ambitious plan.

What we are looking for

Our power sector is in the middle of a major transformation. Its increasingly renewable resource mix and demand-side changes require algorithmic management far beyond what was historically required. Because of this, scalable model development and deployment is at the heart of what EQ does. We are looking for Staff / Sr Staff Software Engineers who are passionate about helping to deliver this scientific platform – to stay at the forefront of AI/ML technology and operationalize those solutions at enterprise scale.

What you will do

You will be a member of EQ’s Science Platform team. Our Science Platform enables our internal data scientists, as well as external customers, to develop, experiment with, deploy, and monitor forecasting and optimization models  sit between our data and infra engineers and our scientists – developing frameworks for model development that are both robust and efficient to iterate within. We help bring the algorithmic capabilities of our scientists to a broad range of customer energy applications.

Key Responsibilities:

• Abstract away the complexities behind deployment and orchestration of a large number of forecasting workflows, enabling a fast model development lifecycle for our Science team

• Integrate with data and compute infrastructure to optimize resource utilization and performance

• Implement automated testing and monitoring for ML models in production

• Maintain and iterate on our model registry and experiment tracking

• Co-design frameworks that support model experimentation, hyperparameter tuning, training, and deployment

• Partner with our Data Services team to incrementally improve our feature store and tie it to the EQ ontology

• Collaborate closely with data scientists to understand new model requirements and together implement solutions that are robust, validated, and scalable

• Collaborate with the Science Platform Simulation team to incorporate forecasting into physical and portfolio asset optimizations

• Partner with our Product and Customer Delivery teams to enable external customers to perform similar tasks to our internal scientists, with minimal code divergence and following security best practices

• Stay up-to-date with the latest advancements in ML engineering and integrate best practices into the platform

The minimum qualifications you’ll need

• A commitment to clean energy and combating climate change

• Proficiency and 5+ years experience in Python software development

• Familiarity with automated build, deployment, and orchestration tools such as CI/CD, Pants, Docker, Metaflow, Argo, and Kubernetes

• Strong understanding of data pipelines, ETL, and data infrastructure

• Experience with observability tooling like Grafana, Honeycomb, and Prometheus

• Experience with common machine learning algorithms and libraries (xgboost, sklearn, pytorch, pandas, polars, pandera)
Prior experience in operationalizing machine learning workflows

• Agility in working with cross-functional teams and adapting to new work methodologies

• Familiarity with agile practices, or a willingness to…