Domain: EV Battery Manufacturing & Electrochemistry
Experience: 5–7 Years
Education: B. Tech/M. Tech in Chemical Engineering or M. Sc. Chemistry
Role Objective
We are seeking a high-calibre Senior Machine Learning Engineer (L5) to bridge electrochemical research and gigafactory-scale manufacturing.
This role demands a rare combination of:
- Strong Chemistry / Chemical Engineering fundamentals
- Advanced Machine Learning & Deep Learning expertise
- Rigorous statistical and probabilistic thinking
You will build physics-informed digital twins to predict battery life, optimize manufacturing yield, and enable intelligent decision-making at scale.
Core Responsibilities
1. Advanced Machine Learning & Deep Learning
- Design and deploy time-series models (Transformers, LSTMs) to analyze battery cycling and degradation patterns
- Develop computer vision systems (CNNs, Vision Transformers) for defect detection in electrode coating and assembly
- Build physics-informed models (PINNs) embedding electrochemical constraints into learning frameworks
- Implement self-supervised and representation learning on large-scale industrial datasets
2. Generative AI & Intelligent Systems (good to have)
- Develop RAG-based systems to extract insights from chemical literature, patents, and technical documents
- Build agentic workflows / multi-agent systems for automated root-cause analysis across plant and lab data
- Enable knowledge-driven AI systems linking process, material, and performance data
3. Statistical Modelling & Scientific Rigor
- Lead Design of Experiments (DOE) for new materials and process optimization
- Apply multivariate statistical analysis (ANOVA, MANOVA) to understand process variability
- Develop probabilistic models (Gaussian Processes, Monte Carlo methods) for:
- Remaining Helpful Life (RUL)
- Battery reliability and uncertainty quantification
- Implement statistical quality control (CUSUM, EWMA) for early drift detection
4. Physics-Informed & Domain-Driven Modelling
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