Senior Machine Learning Engineer - Perception & Embodied AI

General Motors Ventures; Mountain View (CA); On-site; Confidential; Full time

Job summary

General Motors Ventures is seeking a Senior Machine Learning Engineer for Perception within the Embodied AI organization. You will own the end-to-end pipeline for safety-critical perception models, from data curation to real-time deployment on the vehicle’s compute platform.

Responsibilities include 3D object detection and tracking, real-time map detection, multi-modal sensor fusion, and scalable training infrastructure.

Qualifications

• 5+ years in Computer Vision, Deep Learning or Perception in production.
• Hands-on with PyTorch or TensorFlow for training, experimentation and debugging.
• Experience fusing data from Camera, LiDAR, and/or Radar.
• Deploying ML models for real-time embedded systems.

Responsibilities

• Own end-to-end ML perception model lifecycle (design, training, validation, deployment).
• Develop 3D object detection and tracking for vehicles, pedestrians, cyclists.
• Real-time map detection of drivable world (lanes, boundaries, signs).
• Fuse multi-modal sensor data (Camera, LiDAR, Radar).
• Build scalable ML training infra, data mining and multi-stage training.
• Optimize models for low-latency embedded hardware and data iterations.
• Debug performance degradations and edge-case failures with data-driven analysis.
• Define and monitor robust model-level metrics for development.
• Integrate perception outputs with Safety, Systems and other teams.

Skills

Computer Vision

Deep Learning

Perception

PyTorch

TensorFlow

Sensor fusion

Real-time embedded systems

Data analysis

Education

BS/MS/PhD in Computer Science, ML, Robotics

Tools

PyTorch

TensorFlow

Job description

Job Description

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard -from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.

Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.

Role

As a Senior Machine Learning Engineer for Perception within the Embodied AI organization, you will own the end-to-end pipeline for safety-critical ML perception models, from initial research and large-scale data curation to optimization and real-time deployment on the vehicle's compute platform. Your primary mission is to enable the vehicle to accurately and reliably see, classify, and track everything in its environment—from rare road markings and complex intersections to vulnerable road users (VRUs).

Responsibilities

• End-to-End Model Lifecycle: Own the design, training, validation, and deployment of deep learning models for core perception tasks such as:
• 3D Object Detection and Tracking (vehicles, pedestrians, cyclists).
• Real-time map detection of the drivable world (lanes, road boundaries, traffic signs).
• Multi-Modal Sensor Fusion (Camera, LiDAR, Radar).
• Production Pipeline: Build and scale the ML training infrastructure, including data mining and loading, multi-stage training and evaluation, to streamline model development.
• Performance Optimization: Improve model performance through data iterations, parameter tunings, training strategy and architecture updates to produce reliable models that meet and the strict real-time, low-latency requirements on the vehicle's embedded hardware.
• Model Debugging: Conduct rigorous, data-driven analysis to identify, debug, and resolve performance degradations and failures, specifically targeting long-tail and adversarial scenarios (e.g., adverse weather, sensor noise, occlusions).
• Metric Definition: Define and implement robust model-level metrics to aid model development.
• System Integration: Work closely with the Safety, Systems, and other engineering functions to integrate Perception outputs.

Skills & Experience

• BS, MS or PhD in Computer Science, Machine Learning, Robotics, or a related quantitative field.
• 5+ years of professional experience with a focus on Computer Vision, Deep Learning, and Perception in a production environment.
• Deep hands-on experience with modern deep learning frameworks (e.g., PyTorch or TensorFlow) for training, experimentation, and debugging complex DNNs.
• Proven experience working with and fusing data from multiple sensor modalities (Camera, LiDAR, and/or Radar).
• Practical experience deploying and optimizing ML models for resource-constrained, real-time embedded systems.
• Demonstrated ability to drive model improvements through large-scale data analysis, error logging, and data curation.

Bonus:

• Expertise with Transformer-based models for 3D detection, tracking