Staff ML Engineer – Embodied AI Scaling Foundations

General Motors

. Design and run experiments connecting data composition to model behavior, including dataset mixtures, sampling strategies, curricula, and scaling-law studies .

Posted 11/5/2026full-timeUnited StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in designing and running experiments for model behavior analysis, with strong proficiency in Python and PyTorch for training large-scale models. Demonstrated ability in data-centric machine learning, including data curation, evaluation, and clear communication of results.

Highest-signal resume keywords

Python ProficiencyPyTorch ExperienceData-Centric Machine LearningExperiment DesignLarge-Scale Foundation Models

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning FundamentalsExperiment DesignData CurationModel TrainingAblation AnalysisSignal-Versus-Noise EvaluationData AnalysisAuto-LabelingUncertainty EstimationTrajectory Generation

Soft Skills

Clear CommunicationCollaboration

Tools & Technologies

NumPyPandasSQLSpark

Certifications & Qualifications

Master's or PhD in Computer ScienceRoboticsMachine Learning

Industry Keywords

Self-Supervised Pre-TrainingImitation LearningReinforcement LearningFoundation-Model Fine-TuningOn-Road Behavior PredictionMulti-GPU TrainingCross-Functional Collaboration

Tech Stack

Tools & technologies

Node.jsNumpyPandasPythonPyTorchSparkSQL

About the role

Key responsibilities & impact

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 such as auto-labeling, deduplication, difficulty and uncertainty estimation, and long-tail/OOD scenario discovery
Define offline metrics and evaluations that predict on-road behavior and guide model and data decisions
Trace model failures to root causes in the data and specify data needed to fix them
Train models at scale across large multi-GPU and multi-node datasets
Partner 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 incorporate promising advances into recipes and evaluations

Requirements

What you’ll need

Master's or PhD in Computer Science, Robotics, Machine Learning
Strong ML fundamentals, including experiment design, baseline selection, ablation analysis, and signal-versus-noise evaluation
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 models and pre-training, fine-tuning, and alignment
Solid data analysis skills with NumPy and Pandas; SQL or Spark for large datasets
Demonstrated ability to deliver applied ML results under real-world constraints and timelines
Clear communication of results and limitations to engineers and non-experts

Benefits

Comp & perks

Benefits information is provided through GM Total Rewards resources
Reasonable accommodations for applicants with disabilities
Inclusive, non-discriminatory workplace