Senior AI/ML Engineer - Data Scaling, Embodied AI Data Foundations
General Motors1 day ago
Markham, CanadaSenior
Base Salary
$125k - $175k/yr
Responsibilities
- Design experiments involving 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.
- Develop data curation and mining methods including auto-labeling, deduplication, difficulty and uncertainty estimation, and long-tail and out-of-distribution scenario discovery.
- Define offline metrics and evaluations that predict on-road behavior and guide model and data decisions.
- Trace model failures to their data root causes and specify data needed to address them.
- Train models at scale across large multi-GPU and multi-node datasets while partnering with platform teams on 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.
- Strong machine learning fundamentals, including experimental 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 large-scale foundation-model pre-training, fine-tuning, and alignment.
- 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 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 qualifications include experience with robotics, autonomous driving, embodied AI systems, synthetic or simulation data, sim-to-real transfer, or production ML deployment workflows.
Benefits
- Hybrid work arrangement requiring reporting to Markham at least three times per week, or another frequency determined by the business.
- Paid time off including vacation days, holidays, and supplemental pregnancy, parental, and adoption leave benefits.
- Healthcare, dental, and vision benefits, including a healthcare spending account and wellness incentive.
- Life insurance plans for employees and their families.
- Defined Contribution Pension plan with company and matching contributions.
- GM Vehicle Purchase Plan for employees, family, and friends.
Tech Stack
Categories
ML EngineeringRobotics
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.