Note: The job is a remote job and is open to candidates in USA. General Motors is building autonomous driving systems from L2 through L4 using real-world fleet and synthetic simulation data. The Senior AI/ML Engineer will develop data-centric AI solutions, design data curation and model training recipes, and improve safe autonomous driving performance across diverse real-world scenarios.
Responsibilities
- Design and run experiments that connect data composition to model behavior: dataset mixtures, sampling strategies, curricula, and scaling-law studies that tell us where to invest next
- Apply methods such as self-supervised pre-training, imitation learning, reinforcement learning, and foundation-model fine-tuning to driving behavior, trajectory generation, and perception tasks
- Develop data curation and mining methods - auto-labeling, deduplication, difficulty and uncertainty estimation, long-tail and out-of-distribution scenario discovery - to raise the value of every training example
- Define offline metrics and evaluations that actually predict on-road behavior, and use them to make model and data decisions from evidence rather than intuition
- Trace model failures back to their root cause in the data, then close the loop by specifying the data needed to fix them
- Train models at scale across large multi-GPU/multi-node datasets, partnering with platform teams on the pipelines and tooling this requires
- Collaborate with cross-functional teams to bring models into onboard driving systems, and document learnings and best practices along the way
- Follow relevant literature and bring promising advances into our recipes and evaluations
Skills
- Master's or PhD in Computer Science, Robotics, Machine Learning
- Strong ML fundamentals: you can design a clean experiment, pick the right baseline, read an ablation, and tell signal from noise
- Proficiency in Python and PyTorch, with experience training models on large datasets
- Hands-on experience with data-centric ML: curation, sampling, labeling, or evaluation of large training sets
- Working knowledge of large-scale foundation models and how they are pre-trained, fine-tuned, and aligned
- Solid data analysis skills (NumPy, Pandas; SQL or Spark for large datasets)
- Demonstrated ability to deliver applied ML results under real-world constraints and timelines
- Clear communication: you can explain a result and its limits to both engineers and non-experts
- PhD, publications, or open-source contributions in representation learning, multimodal or vision-language models, generative models, RL, or data-centric ML
- Experience with robotics, autonomous driving, or other embodied AI systems
- Experience with synthetic and simulation data, including sim-to-real transfer
- Familiarity with production ML deployment workflows
Benefits
- Medical insurance
- Dental insurance
- Vision insurance
- Health Savings Account
- Flexible Spending Accounts
- Retirement savings plan
- Sickness and accident benefits
- Life insurance
- Paid vacation and holidays
- Tuition assistance programs
- Employee assistance program
- GM vehicle discounts
- Potential eligibility for relocation benefits
- Fully remote or hybrid work arrangement
Company Overview
Company H1B Sponsorship