Machine Learning Engineer, Equilibrium Energy

• A commitment to clean energy and combating climate change
• Proficiency in Python software development
• Familiarity with automated build, deployment, and orchestration tools such as CI/CD, Pants, Docker, Metaflow, 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 learn
• Strong communication skills for collaborating within a remote-first team that works internationally across timezones

Desirable

• We are looking for experienced ML 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
• 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 at scale
• We 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
• Develop and maintain scalable ML pipelines, used to support forecasting and optimization models
• Design frameworks that support model experimentation, hyperparameter tuning, training, and deployment
• Collaborate closely with data scientists to understand new model requirements and together implement solutions that are robust, validated, and scalable
• Integrate with data and compute infrastructure to optimize resource utilization and performance
• Implement automated testing and monitoring for ML models in production
• 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