Senior ML Engineer - Data-Centric Autonomous Driving (Remote)

Aimlroles; Northern (KY); Hybrid; USD 159,000 - 231,000; Full time

Benefits offered by this job

Health benefits

GM vehicle discounts

Tuition assistance

Employee assistance program

Job summary

General Motors is seeking a Senior AI/ML Engineer in Embodied AI Data Foundations to develop data-centric AI solutions that improve autonomous driving performance. You will design data curation and model training recipes to ensure safe, reliable behavior across diverse real-world scenarios, using real and synthetic data.

You will run experiments, apply pre-training and fine-tuning methods, and collaborate with cross-functional teams to deploy models into onboard driving systems while documenting

Qualifications

• Must design clean experiments, select baselines, read ablations and identify signal from noise.
• Experience training models on large datasets and with Python/PyTorch.
• Hands-on data-centric ML: curation, sampling, labeling, or evaluation of large training sets.
• Familiarity with large-scale foundation models and pre-training/fine-tuning processes.

Responsibilities

• Design and run experiments linking data composition to model behavior.
• Apply self-supervised pre-training, imitation learning, RL, and foundation-model fine-tuning to driving tasks.
• Develop data curation and mining methods to raise training data value.
• Define offline metrics and evaluations predicting on-road behavior from evidence.

Skills

Python

PyTorch

Data-centric ML

Large datasets

NumPy/Pandas

SQL/Spark

Communication

Education

Master's or PhD in Computer Science/Robotics/ML

Tools

NumPy

Pandas

SQL

Spark

Job description

General Motors is seeking a Senior AI/ML Engineer in Embodied AI Data Foundations to develop data-centric AI solutions that improve autonomous driving performance. You will design data curation and model training recipes to ensure safe, reliable behavior across diverse real-world scenarios, using real and synthetic data.

You will run experiments, apply pre-training and fine-tuning methods, and collaborate with cross-functional teams to deploy models into onboard driving systems while documenting