Forward Deployed Engineer, Machine Learning

We're looking for a hands-on Machine Learning Engineer to join a growing AI team at a multi-billion-dollar cleantech company undergoing a major digital transformation.

You'll work on challenging problems that combine machine learning, mathematics, and software engineering. You'll develop models from scratch, bring them into production, and build solutions that make a meaningful impact across the business.

This is an opportunity for someone who enjoys solving complex technical problems, working closely with different teams, and building technology that goes beyond traditional AI applications.

• Design, develop, and deploy machine learning models from the ground up.
• Build solutions involving forecasting, optimization, and mathematical modeling.
• Work with large datasets to develop scalable, production-ready ML systems.
• Collaborate with engineering and business teams to identify problems that can be solved using ML and AI.
• Own the full ML lifecycle, from problem definition and model development to deployment and continuous improvement.
• Write high-quality Python code and contribute to software architecture and system design.
• Evaluate and improve model performance, accuracy, and reliability.

• Strong understanding of machine learning fundamentals, probability, statistics, and mathematics.
• Hands-on experience building and training ML models from scratch.
• Strong Python programming skills and software engineering experience.
• Experience developing and deploying machine learning systems in production.
• Familiarity with ML frameworks such as PyTorch, JAX, or similar tools.
• Experience with forecasting, time-series modeling, optimization, tabular data, or deep learning.
• Ability to work independently and collaborate with cross-functional teams to solve complex problems.

Nice to have:

• Experience with mathematical or convex optimization.
• Familiarity with CVXPY, Pyomo, Gurobi, or similar optimization tools.
• Experience with physics-based modeling or state estimation.
• Strong understanding of deep learning architectures and theory.
• Experience with LLMs, foundation models, or agentic AI systems.