Staff ML Engineer - Embodied AI Scaling Foundations

General Motors • Sunnyvale, CA

2d

About The Position

The Staff ML Engineer will be an individual contributor in General Motors’ Embodied AI Data Foundations organization, developing data-centric AI solutions that directly improve autonomous driving performance. The role designs and runs data-curation and model-training recipes to produce models capable of safe, reliable behavior across diverse real-world scenarios, using both real and synthetic data.

The Data Scaling team owns the data flywheel for autonomous-vehicle foundation-model pre-training and supervised fine-tuning. It determines what data models need to learn driving behaviors at scale, defines data quality across the development loop, and develops ML models that improve driving behavior through better data composition. The team works with large existing GM datasets and helps define future high-value datasets, aiming to increase the effective data behind each major model release.

Requirements

• Master's or PhD in Computer Science, Robotics, or Machine Learning.
• Strong machine-learning fundamentals, including the ability to design experiments, select baselines, interpret ablations, and distinguish signal from noise.
• Proficiency in Python and PyTorch.
• Experience training models on large datasets.
• Hands-on experience with data-centric machine learning, including curation, sampling, labeling, or evaluation of large training sets.
• Working knowledge of large-scale foundation models and their pre-training, fine-tuning, and alignment.
• Data-analysis skills using NumPy and Pandas, and SQL or Spark for large datasets.
• Demonstrated ability to deliver applied machine-learning results under real-world constraints and timelines.
• Clear communication skills, including the ability to explain results and their limitations to engineers and non-experts.

Nice To Haves

• PhD, publications, or open-source contributions in representation learning, multimodal or vision-language models, generative models, reinforcement learning, or data-centric machine learning.
• Experience with robotics, autonomous driving, or other embodied AI systems.
• Experience with synthetic and simulation data, including sim-to-real transfer.
• Familiarity with production machine-learning deployment workflows.

Responsibilities

• Design and run 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, including auto-labeling, deduplication, difficulty and uncertainty estimation, and discovery of long-tail and out-of-distribution scenarios.
• Define offline metrics and evaluations that predict on-road behavior, and use evidence to guide model and data decisions.
• Trace model failures to their 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, partnering with platform teams on required pipelines and tooling.
• Collaborate with cross-functional teams to bring models into onboard driving systems.
• Document learnings and best practices.
• Follow relevant literature and bring promising advances into model-training recipes and evaluations.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior