Staff ML Engineer - Embodied AI Scaling Foundations
General Motors1 day ago
Responsibilities
- Design experiments connecting data composition to model behavior, including dataset mixtures, sampling strategies, curricula, and scaling-law studies.
- Apply 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 such as auto-labeling, deduplication, difficulty and uncertainty estimation, and long-tail or out-of-distribution scenario discovery.
- Define offline metrics and evaluations that predict on-road behavior and use evidence to guide model and data decisions.
- Trace model failures to root causes in the data and specify the data needed to address them.
- Train models at scale across large multi-GPU and multi-node datasets while partnering with platform teams on required pipelines and tooling.
- Collaborate with cross-functional teams to bring models into onboard driving systems and document learnings and best practices.
- Follow relevant literature and incorporate promising advances into training recipes and evaluations.
Requirements
- Master's or PhD in Computer Science, Robotics, or Machine Learning is required.
- Strong machine learning fundamentals, including experiment design, baseline selection, ablation analysis, and signal-versus-noise assessment.
- Proficiency in Python and PyTorch, with experience training models on large datasets.
- Hands-on experience with data-centric ML, including curation, sampling, labeling, or evaluation of large training sets.
- Working knowledge of how large-scale foundation models are pre-trained, fine-tuned, and aligned.
- Data analysis skills using NumPy and Pandas, plus SQL or Spark for large datasets.
- Demonstrated ability to deliver applied ML results under real-world constraints and timelines.
- Ability to communicate results and limitations clearly to engineers and non-experts.
- Preferred qualifications include a PhD, publications, or open-source contributions in representation learning, multimodal or vision-language models, generative models, reinforcement learning, or data-centric ML.
- Preferred experience includes robotics, autonomous driving, embodied AI systems, synthetic and simulation data, sim-to-real transfer, and production ML deployment workflows.
Benefits
- GM provides well-being support from day one through its Total Rewards resources.
Tech Stack
Categories
About General Motors
General Motors designs, manufactures, and sells cars, trucks, and electric vehicles for consumers and commercial fleets under brands including Chevrolet, GMC, Cadillac, and Buick. A public company on the NYSE headquartered in Detroit and founded in 1908, it operates globally and is developing EVs on its Ultium battery platform. Revenue comes from vehicle and parts sales, connected services such as OnStar, and financing through GM Financial.