Software Engineer III, 3D Modelling, XR
Indexed description
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with software development in Python or C++ programming languages, or 1 year of experience with an advanced degree.
- 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Experience developing accessible technologies.
- Experience in Machine Learning methods with a strong background in 3D geometry.
- Proficiency in building and deploying models for both on-device and server environments.
- Demonstrated ability to independently handle data generation, model training, and optimization pipelines.
As a Machine Learning Engineer with a focus on 3D geometry, you will be responsible for the entire model life cycle from data generation to model optimization. You will bridge the gap between advanced research in monocular depth modeling and the real-world requirements for robustness in video and user interface environments.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.
Responsibilities
- Manage the end-to-end training process for high-impact machine learning models.
- Develop and optimize models capable of running efficiently on both consumer devices and server-side infrastructure.
- Own the robustness and data quality of depth models to resolve critical stability and user experience challenges.
- Adapt research in monocular depth modeling to ensure reliable performance across real-world scenarios.
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