Battery Machine Learning Algorithm Engineer

What you'll do

• Develop ML and adaptation algorithms for State of Health, State of Power, and State of Charge estimation.
• Build ML-enhanced predictive models of user load patterns and coupled electro-thermal dynamics.
• Design advanced control algorithms and multi-objective optimization frameworks for real-time battery management.
• Develop physics-informed ML approaches combining physics-based models with data-driven methods.
• Deploy algorithms on resource-constrained embedded platforms using model compression and fixed-point arithmetic.
• Implement robust algorithms capable of handling sensor noise, missing data, and distribution shifts.

What we're looking for

• Experience in machine learning (RNNs, LSTMs, Transformers), control theory, and optimization (MPC, Kalman filters, convex/nonconvex optimization).
• Experience in MATLAB/Simulink.
• Experience in C/C++ for embedded systems and Python & ML frameworks (PyTorch, TensorFlow).
• PhD and 10+ years relevant industry experience (preferred).
• Mathematical modeling experience for nonlinear dynamical systems (preferred).
• Experience with physics-informed neural networks or hybrid physics/ML approaches (preferred).
• Experience with MPC implementation and real-time optimization (preferred).