Software Engineering Manager, Automotive AI Agent - Google Careers
Listed on 2026-02-28
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Software Development
AI Engineer, Software Engineer, Machine Learning/ ML Engineer
Software Engineering Manager, Automotive AI Agent Advanced
Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders;deep expertise in domain.
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- Bachelor's degree or equivalent practical experience.
- 8 years of experience managing and leading software engineering teams.
- Experience in general-purpose programming languages (e.g., C++, Java, Python, or Go).
- Experience building and maintaining distributed systems, cloud APIs, and mobile or embedded SDKs.
- Experience with AI/ML technologies, Large Language Models (LLMs) or Natural Language Understanding (NLU).
- Experience in Large Language Models (LLMs)-based applications and technologies.
- Experience in productizing and productionalizing applied ML/NLP systems.
- Ability to optimize systems, ensuring safety and quality in a safety-critical automotive environment.
- Passion for innovations and converting new technologies (e.g., Live Mode or Agentic workflows) into product value.
Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team.
Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.
With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.
Join the Automotive team as we build the leading development platform for the AI-defined vehicle. We provide automotive OEMs with a Gemini-native platform to build, deploy, and maintain intelligent agents. By adding vehicle integration with Gemini’s reasoning, we empower automakers to deliver proactive and personalized experiences that connect the driver’s life inside and outside the car. As we move toward a "Gemini First" architecture and "Live Mode" orchestration, you will help us define the future of in-car interaction, ensuring our solutions are fully functional, localized for global production.
Applied AI builds conversational agents deployed at a large scale that achieve very meaningful results in the real world. Some examples include the customer agent built for large call center environments, to fast food ordering handled by our Food AI agent. The team is transforming how enterprises connect with customers through the power of AI. We also offer unique experiences for team members where you get to work directly with the model builders (Google Deep Mind / Vertex), learn and work with brilliant AI leaders, and have access to Global 1000 customers via our existing Google Cloud relationships.
The opportunity in this space is tremendous.
- Lead a team of engineers to architect and deploy the Gemini first platform, shifting towards Large Language Models (LLM)-native orchestration and bidirectional streaming capabilities.
- Drive production reliability and stability initiatives to ensure reliability, low latency, and hermetic deployment environments for real-world automotive use.
- Lead the development of omnichannel SDKs (including Android, iOS, and embedded) to support seamless experiences across the vehicle and companion apps.
- Partner with product and OEMs to translate requirements such as multi-agent orchestration and offline/online arbitration into production-ready software.
- Employ a data-driven approach to instrument metrics and improve the quality of the driver experience.
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