About the job
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.
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 impactful product launches.
Qualifications
Minimum
Bachelor’s degree or equivalent practical experience.
2 years of experience with software development in one or more programming languages.
2 years of experience in AL/ML (e.g., deep learning, perception, or computer vision).
Experience in C++, Python, Generative AI, Machine Learning.
Preferred
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
Experience with multimodal learning, large language models, or AI agents.
Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.
Experience with prompt engineering, few-shot learning, post-training techniques, and evaluations.
Familiarity with large-scale model training and deployment.