Senior Machine Learning Engineer

EVOLVE Intelligence accelerates the transition toward a carbon-free future through software and analytics. We are looking for a Senior Machine Learning Engineer to help us accomplish this mission.

Working with the Analytics & Data Science team in DNV - Energy Management's Technology group is more than just a job; it's an opportunity to be part of a collaborative community where you can learn, grow, and thrive. Join a dynamic and diverse technology team that values innovation, impact, and sustainability. Help us build intelligent, scalable ML solutions that support demand side management, demand flexibility, and transportation electrification programs!

As a Senior Machine Learning Engineer, you will develop and deploy robust machine learning systems that power analytics, optimize energy efficiency, and advance the clean energy transition. You'll work closely with data scientists, data engineers, analytics engineers, and software developers to take models from experimentation to production, ensuring they are performant, maintainable, and impactful.

Your work will play a vital role in enabling utilities and clean energy programs to make data-driven decisions that reduce emissions, meet goals, and shape a sustainable future.

This role is based at our any DNV office in US, presenting a dynamic hybrid schedule where employees will typically spend three (3) days per week working from either a DNV office or client location/site. Further details regarding role-specific requirements will be shared during the interview process.

What You'll Do:

• Develop & Deploy ML Models: Design, train, and productionize models using structured and unstructured datasets
• Operationalize ML Workflows: Build and maintain ML pipelines using Databricks, Azure ML, MLflow, or similar platforms
• Collaborate with Data & Software Teams: Work closely with data engineers and software developers to integrate models into end-user applications and systems
• Monitor & Improve Model Performance: Implement monitoring, retraining strategies, and evaluation metrics to ensure ongoing model accuracy and stability
• Automate & Scale: Leverage cloud-native tools to deploy scalable ML services and APIs
• Champion Best Practices: Contribute to team-wide ML Ops, version control, testing, and documentation practices
• Mentor & Share Knowledge: Guide junior engineers and in best practices, reviewing code and modeling approaches
• Explore & Innovate: Research and experiment with new modeling techniques that enhance predictive performance and operational impact