Senior ML Engineer Perception

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. Were turning todays impossible into tomorrows 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 peopleas we aim to makedriving 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 vehicles compute platform. Your primary mission is to enable the vehicle to accurately and reliably see classify and track everything in its environmentfrom 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 vehicles 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 and scene understanding.

• Technical leadership experience including mentoring junior engineers and leading major feature development from concept to launch.

• Contributions to relevant academic publications (CVPR ECCV ICCV IROS NeurIPS etc.).

About GM

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