ML Engineer (Part-time / Freelancer)
ML Engineer (Part-time / Freelancer)
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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 youre interested in the EV or battery analytics domain we welcome your application.
Engagement Model:
Flexible remote work
Duration: 24 months (extendable)
This is a part-time or freelance position.
Remote Work :
Yes
Employment Type :
Part-time
Employment Type
Remote
