Software Engineer, Multimodal AI, XR

About the job

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.

We are seeking a highly motivated and innovative software engineer focused on advancing in multimodal and conversational AI for next-gen 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 more capable AI for XR devices (glasses, goggles). This role offers a unique opportunity to contribute to AI research and seeing your work define the next-generation 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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + 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 like prompting, few-shot learning, and post-training (e.g., quantization) to enhance performance and robustness while adhering to strict device power and latency constraints.
• Create comprehensive evaluation plans for hybrid systems, from dataset development to defining KPIs that measure 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 and 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 impactful product launches.

Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
• 1 year of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
• 1 year of experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.

Preferred qualifications:

• Master's degree or PhD in Computer Science, or related technical fields.
• Experience with multimodal learning, large language models, or AI agents.
• Experience with prompt engineering, few-shot learning, post-training techniques, and evaluations.
• Familiarity with on-device model training and deployment.