Senior Machine Learning Engineer
Aptiv (company site, opens in a new tab)6–8 yrsBengaluruOn-siteDirect career pageEst. 22–35 LPA
Posted on 18 Sept
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Job description
Structured overview of role & requirements
About This Role
- Design, develop, train, and validate ML and Deep Learning models for ADAS/Automotive validation use cases using large-scale vehicle datasets.
- Build and maintain end-to-end ML pipelines covering data acquisition, preprocessing, labeling, feature engineering, model training (including distributed training on AWS/Azure/HPC), and performance evaluation.
- Collaborate with algorithm and validation teams to improve model robustness, conduct root-cause analysis on failures, and support deployment readiness and validation workflows.
Minimum Requirements
- Bachelor's or Master's degree in CS, AI, Machine Learning, Data Science, Electrical or Computer Engineering, or related field.
- 6-8 years of industry experience in Machine Learning, Data Science, Deep Learning, or related fields.
- Strong Python development skills with hands-on experience building production-grade ML pipelines and training models from scratch with large-scale datasets.
- Experience with automotive domain signals, ECUs, CAN, Ethernet, and embedded systems fundamentals.
Ideal Candidate Profile
- Experienced in end-to-end ML pipeline development focusing on perception, signal processing, and validation in automotive environments.
- Familiar with distributed training, model evaluation, validation frameworks, and managing large-scale automotive datasets.
- Skilled in collaborating across algorithm, systems, and validation teams to enhance model robustness and solve complex ML problems in ADAS contexts.