ML Engineer (Part-time / Freelancer)

Job Description

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.

Qualifications

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

Additional Information

If you’re interested in the EV or battery analytics domain, we welcome your application.

Engagement Model:

• Flexible remote work

• Duration: 2–4 months (extendable)

This is a part-time or freelance position.