Part-Time ML Engineer (Part-time)

Responsibilities:

• Build, train, and benchmark ML models (e.g. LSTM, XGBoost) for battery State-of-Health (SOH), Remaining Useful Life (RUL), and degradation prediction.
• Analyze large-scale battery performance data to extract actionable insights.
• Develop anomaly and event detection algorithms for safety-critical battery threats.
• Create predictive maintenance models to forecast battery failures and degradation.
• Validate and benchmark model performance using real-world and simulated datasets.
• Work with the product team to ensure integration of developed models.
• Stay updated with the latest research in battery modeling and machine learning techniques.
• Research and collaborate with the team to incorporate electrochemical knowledge into ML models.

Preferred Skills & Tools:

• Python (Pandas, NumPy, Scikit-learn, PyTorch or TensorFlow)
• Strong grasp of time-series modeling, signal processing, and statistical evaluation
• Familiarity with LSTM, GRU, transformer models, or forecasting pipelines
• Hands-on experience with deep learning models (CNN, DNN, RNN), NLP techniques, and sequence models
• Exposure to modern LLM ecosystems (ChatGPT, Gemini, Ollama) and tools like LangChain, AI agents, and prompt engineering
• Good understanding of battery systems: SOC, SOH, DOD, thermal behaviour, lifecycle stressors
• Experience working with EV, IoT, or BMS datasets (even in academic projects)
• SQL basics for querying historical telemetry

Nice to Have:

• Familiarity with Kalman filters, physics-informed models, or hybrid ML-physics methods
• GitHub portfolio or academic project code repos
• Knowledge of EV architectures or energy storage standards

What is the last date for applying to the job?

Which countries are accepted for this remote job?