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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 sensing.\\nBuild 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 efficiency.\\nDesign 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 margins.\\nCollaborate with system teams to co-optimize battery algorithms with device power management and thermal systems.\\nDevelop physics-informed ML approaches that combine physics-based models with data-driven methods to improve generalization and ensure physically plausible predictions.\\nDeploy 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 experience\\nMathematical modeling experience for nonlinear dynamical systems\\nExperience in C/C++ for embedded systems and Python \u0026 ML frameworks (PyTorch, TensorFlow)\\nExperience with physics-informed neural networks or hybrid physics/ML approaches\\nExperience with MPC implementation and real-time optimization
