Developing scalable model development and deployment solutions for the power sector's increasingly renewable resource mix and demand-side changes.
Requirements
- 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
- Familiarity with Databricks, Spark, and dbt
Responsibilities
- Develop and maintain scalable ML pipelines, used to support forecasting and optimization models
- Design frameworks that support model experimentation, hyperparameter tuning, training, and deployment
- Integrate with data and compute infrastructure to optimize resource utilization and performance
- Implement automated testing and monitoring for ML models in production
- Partner with Product and Customer Delivery teams to enable external customers to perform similar tasks to internal scientists
- Stay up-to-date with the latest advancements in ML engineering and integrate best practices into the platform
- Collaborate closely with data scientists to understand new model requirements and implement solutions
Other
- Commitment to clean energy and combating climate change
- 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
- Advanced degree in computer science or machine learning (nice-to-have)
- Background in the energy and power systems sector (nice-to-have)
- Unlimited vacation and flexible work schedule
- Ability to work remotely from anywhere in the United States, Canada & Europe, or join one of our regional hubs in Boston, SF Bay Area, or London
