Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
The Google XR mission is to make information accessible. The team is working to advance artificial intelligence for wearable computers.
In this role, you will conduct development on large language models (LLMs) and agents, particularly in the multimodal domain (vision, audio), focusing on developing AI for XR devices (glasses, goggles). You will contribute to AI research and the development of computing products.
For decades, the computing revolution has reshaped our world driven by breakthroughs in compute, connectivity, mobile, and now, AI. Google's XR team is at the forefront of the next major leap – the convergence of AI and XR. This is more than just new devices – it's about reimagining how we interact with the world around us. We're building a future where lightweight XR devices like smart glasses and headsets pair with helpful AI to augment human intelligence, offering personalized, conversational, and contextually aware experiences.
The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Lead the development and optimization of on-device and hybrid multimodal models for XR devices. Utilize techniques to enhance performance and robustness while adhering to strict device power and latency constraints. Write production-quality C++ and Python code.
- Create comprehensive evaluation plans for hybrid systems, from dataset development to defining KPIs that measure both model accuracy and on-device efficiency.
- Identify, implement, and ship the latest modeling innovations, focusing on hybrid agent architectures, orchestration between edge and cloud, multimodality, tool integrations, and personalization.
- Prove out concepts for on-device AI features through rapid prototyping and iterative development, facilitating team testing in close partnership with XR product teams.
- Work closely with research scientists, engineers, and product teams to drive the technical roadmap. Foster a collaborative environment and share findings through conference publications while contributing to product launches.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 3 years of experience in AI/Machine Learning (e.g., deep learning, perception, or computer vision).
- Experience in C++, Python, Generative AI, Multimodal Machine Learning.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.
- Experience with multimodal learning, large language models, or AI agents.
- Experience with prompt engineering, few-shot learning, post-training techniques, and evaluations.
- Familiarity with large-scale model training and deployment.