Staff AI/ML Engineer - Future Sensing, Embodied AI

General Motors (GM)

Remote Hybrid

Quick summary

Work type: Remote
Location: Sunnyvale, CA
Salary: $189,300–$320,700 / yr
Posted: 3 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $208k

This role $255k

$152k most similar roles pay here $339k

This role pays more than 84% of similar roles. Most pay $170,000–$246,150 — the shaded band above. At the midpoint, this role pays about $255k versus about $208k for comparable roles.

Based on 240 similar postings.

Employer

About General Motors (GM)

General Motors (GM) is a leading American multinational automotive corporation founded in 1908 and headquartered in Detroit, Michigan.

General Motors (GM) currently has 131 open roles on FindRole.

Listed pay typically runs $170,000–$258,500 across 75 roles with salary data.

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At a glance

TL;DR · Staff AI/ML Engineer - Future Sensing, Embodied AI

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As a Staff AI/ML Future Sensing Engineer at General Motors' Embodied AI team, you will serve as a senior individual contributor driving end-to-end technical work that informs next-generation sensing architecture decisions for autonomous vehicles. Your responsibilities include designing and implementing machine learning solutions aligned with GM’s objectives, leading technical studies on sensor selection and multi-modal fusion, and architecting perception models for detection and localization. You will also define robust metrics to evaluate performance parity relative to existing architectures, drive system integration across various engineering domains, and support the continuous improvement of GM’s autonomy stack. The ideal candidate has extensive experience with deep learning frameworks like PyTorch, proficiency in Python, and strong data processing skills using tools such as NumPy and Pandas. Additionally, you should have a background in robotics or autonomous driving systems and experience deploying ML models for production environments.

Skills

Python PyTorch NumPy Pandas Apache_Spark multi-modal_sensor_fusion deep_learning_frameworks model_training_pipelines data_processing model_validation debugging performance_optimization error_analysis simulation synthetic_data sensor_fusion perception_pipelines system_integration CI/CD

What you'll do

Lead end-to-end technical studies on sensor selection and multi-modal fusion for autonomous vehicles.
Architect and evaluate perception models supporting detection, tracking, localization, and uncertainty estimation.
Define robust metrics to compare sensor configurations and subsystem differences in autonomous driving systems.
Drive system integration across sensing, calibration, compute, software architecture, and vehicle constraints.
Design efficient infrastructure for large-scale data processing and model training in AI/ML projects.

What we're looking for

Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Machine Learning, Electrical Engineering, or related field.
Strong experience building and scaling AI/ML systems for perception, autonomy, robotics, or real-world systems.
Deep hands-on experience with modern deep learning frameworks like PyTorch and proficiency in Python.
Experience with multi-modal sensor fusion and perception pipelines using cameras, radar, lidar, or similar sensing modalities.
Strong data processing skills using tools such as NumPy, Pandas, Apache Spark.
Experience defining metrics and evaluation methodologies for perception or autonomy systems.
Technical leadership experience including mentoring engineers and shaping major workstreams from concept to execution.