Staff Software Engineer, Tech Lead, Multimodal AI for Glasses and XR

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.

For decades, the computing revolution has reshaped our world driven by breakthroughs in compute, connectivity, mobile, and now, AI. The 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.

• Architect, design, and build distributed, asynchronous AI systems that seamlessly span wearable smart glasses, mobile devices, and cloud infrastructure, managing real-time multimodal streaming, state management, buffering, backpressure, and fault tolerance.
• Lead the end-to-end integration of computer vision, language, and multimodal models into production systems, designing routing and decision-making mechanisms grounded in model uncertainty, confidence calibration, abstention, and verification.
• Drive system-wide performance and reliability across device, mobile, and cloud boundaries, making architectural trade-offs across latency, bandwidth, compute, memory, battery life, thermal limits, and inference cost.
• Partner with machine learning researchers and hardware teams to co-design constrained edge and mobile inference pipelines, leveraging quantization, model compression, and hardware acceleration, alongside scalable cloud AI serving and service orchestration.
• Establish evaluation frameworks, production observability, and debugging tools to measure and improve end-to-end system performance and user experience.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in C++ or Python, modern ML frameworks (e.g., JAX, PyTorch), and on-device/cloud inference runtimes.
• Experience optimizing latency, bandwidth, reliability, compute, memory, energy, and cost constraints across device, mobile, and cloud layers.
• Experience in asynchronous architectures, streaming data, state management, buffering, backpressure, fault tolerance, observability, and production debugging.

Preferred qualifications:

• PhD in Distributed Systems for advanced Machine Learning or Embedded Systems.