The Role
As a Principal Software Engineer in the Vehicle AI division, you will be the technical cornerstone of our smart cabin initiatives. You will architect, design, and deploy low-latency, high-performance AI software that runs directly on edge hardware within the vehicle. You won't just be writing code; you will define the technical roadmap, mentor senior engineers, and collaborate across hardware, UI/UX, and vehicle software teams to bring intelligent features-like natural language voice assistants, driver monitoring systems (DMS), and predictive cabin personalization-to life.
Key Responsibilities
- Architectural Leadership: Design scalable, secure, and real-time software architectures for AI-driven features running on automotive-grade compute platforms (e.g., Qualcomm Snapdragon Digital Chassis, NVIDIA DRIVE).
- Edge AI Optimization: Lead the deployment and optimization of machine learning models (LLMs, computer vision, audio processing) for resource-constrained edge devices using TensorRT, ONNX, or similar frameworks.
- System Integration: Oversee the integration of AI pipelines with foundational infotainment operating systems, particularly Android Automotive OS (AAOS) and QNX.
- Cross-Functional Strategy: Partner with product managers, data scientists, and hardware engineers to balance feature ambition with compute constraints.
- Mentorship & Excellence: Elevate the engineering culture by establishing best practices for code quality, CI/CD, rigorous testing, and system performance profiling. Set the standard for technical excellence within the division.
- Prototyping: Rapidly prototype new AI concepts and evaluate emerging frameworks to keep GM at the cutting edge of automotive technology.
Minimum Qualifications
- Experience: 10+ years of professional software engineering experience, with at least 3+ years in a technical leadership or architectural role.
- Programming: Expert-level proficiency in modern C++ (C++14/17/20) and Python.
- Domain Expertise: Proven track record of shipping commercial software in automotive infotainment, robotics, consumer electronics, or other deeply embedded systems.
- AI/ML Deployment: Hands-on experience optimizing and deploying ML models to edge hardware (NPU/GPU/DSP utilization, quantization, pruning).
- OS Knowledge: Deep understanding of POSIX-compliant operating systems, Linux internals, or RTOS (QNX, VxWorks).
- Education: Bachelor's degree in Computer Science, Computer Engineering, or a related technical field (or equivalent practical experience).
Preferred Qualifications
- Extensive experience with Android Automotive OS (AAOS), specifically Vehicle HAL (VHAL) and native C++ services.
- Experience building or integrating advanced Voice Assistants (ASR, NLU, TTS) or Driver Monitoring Systems (DMS) into embedded environments.
- Familiarity with automotive functional safety standards (ISO 26262, ASIL) and cybersecurity protocols.
- Advanced degree (Master's or Ph.D.) focusing on Artificial Intelligence, Machine Learning, or Embedded Systems.