Battery Machine Learning Algorithm Engineer

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We are looking for an engineer who will develop novel machine learning algorithms control strategies and on-device implementations to improve the utility and intelligence of battery systems.

Develop ML and adaptation algorithms for State of Health State of Power and State of Charge (SOH/SOP/SOC) estimation characterizing degradation mechanisms (capacity fade impedance growth) and peak power capability with robustness across temperature cell aging dynamic loads and inaccurate current ML-enhanced predictive models of user load patterns system power demand and coupled electro-thermal dynamics enabling proactive thermal-aware battery algorithms that extend runtime and improve power advanced control algorithms (MPC adaptive learning-based) and multi-objective optimization frameworks to optimize battery management and power delivery in real time balancing runtime cycle life thermal and system constraints and safety with system teams to co-optimize battery algorithms with device power management and thermal physics-informed ML approaches that combine physics-based models with data-driven methods to improve generalization and ensure physically plausible algorithms on resource-constrained embedded platforms using model compression (quantization pruning distillation) and fixed-point arithmetic with graceful fallback under sensor noise missing data and distribution shift.

Experience in machine learning (RNNs LSTMs Transformers) control theory and optimization (MPC Kalman filters convex/nonconvex optimization)nExperience in MATLAB/Simulink

PhD and 3 years relevant industry experiencenMathematical modeling experience for nonlinear dynamical systemsnExperience in C/C for embedded systems and Python u0026 ML frameworks (PyTorch TensorFlow)nExperience with physics-informed neural networks or hybrid physics/ML approachesnExperience with MPC implementation and real-time optimization