Principal Software Engineer - Vehicle AI

Job Description Work Arrangement: This role is categorized as Remote/ hybrid . This means the successful candidate is expected to report to Austin, TX , Mountain View, CA or Remote (Washington State) three times per week at minimum or other frequency dictated by the business.

The Role General Motors is building the future of intelligent in-cabin experiences, and we are looking for a Principal Software Engineer to serve as a technical leader and authority for our next-generation In-Vehicle AI Assistant platform. Focused on cabin intelligence, voice interaction, and conversational AI, this role bridges the entire edge-to-cloud spectrum—spanning the Android Automotive OS (AAOS) client application layer, cloud AI orchestration pipelines, and backend service integration.

In this high-impact role based out of our Mountain View, CA or Seattle, WA offices, you will shape the multi-year technical vision for our in-cabin assistant platform. You will work extensively across cross-functional boundaries—aligning Cloud AI/ML, In-Cabin UX, Vehicle Platform, Security, and Infrastructure teams to build a cohesive, scalable ecosystem. If you excel at driving technical strategy across complex distributed systems and bringing clarity to large-scale engineering initiatives, this role offers an unmatched opportunity to define the assistant experience for millions of vehicles worldwide.

What You’ll Do

• Define and own the end-to-end architecture for GM’s In-Vehicle AI Assistant platform across both the Android client application layer and cloud backend integration services.
• Drive cross-functional technical strategy, collaborating with Cloud AI/ML, In-Cabin UX, Product Management, Security, and Data Engineering teams to align system boundaries and interface contracts.
• Architect the hybrid edge-cloud orchestration layer, optimizing trade-offs between on-device latency, network bandwidth, cloud compute cost, and offline reliability.
• Establish engineering standards and API protocols for high-performance, real-time client-server communication using gRPC and streaming audio/text interfaces.
• Evaluate and integrate emerging Artificial Intelligence / Machine Learning (AI/ML) agent frameworks, large language model orchestrators, and tool execution engines into the in-cabin assistant ecosystem.
• Unblock critical architectural dependencies across multiple engineering pods, resolving complex trade-offs in system reliability, privacy, and data streaming.
• Drive platform-wide testing, evaluation pipelines, and telemetry standards to measure end-to-end voice latency, response accuracy, and system health.
• Mentor and grow Staff and Senior engineers across client and backend disciplines, fostering a culture of technical rigor and architectural excellence.

Your Skills & Abilities (Required Qualifications)

• Bachelor’s degree in Computer Science, Computer Engineering, or equivalent practical experience.
• 12+ years of professional software development experience, with a proven track record of designing large-scale distributed systems spanning client applications and cloud microservices.
• Broad technical depth across client-side Android architecture (Kotlin/Java) and backend cloud integration (microservices, REST, gRPC, streaming APIs).
• Demonstrated history of cross-functional (XFN) technical leadership, driving architectural alignment and consensus across multiple engineering organizations.
• Deep expertise in client-server interaction patterns, state synchronization, and low-latency network communication protocols.
• Proven ability to define multi-year technical roadmaps and translate high-level product vision into concrete, scalable platform architectures.

What Can Give You a Competitive Advantage (Preferred Qualifications)

• Master’s or Ph.D. in Computer Science, Electrical Engineering, or a related field.
• Technical domain expertise in conversational AI architectures, voice assistant frameworks, or agentic LLM orchestration layers.
• Familiarity with Android Automotive OS (AAOS) applications or in-cabin system constraints.
• Background in edge-cloud hybrid computing, including client-side machine learning execution and server-side model streaming.
• Exceptional technical communication skills, with a track record of articulating complex architectural trade-offs to senior leadership and non-technical stakeholders.

Compensation The compensation information is a good faith est…