Software Engineer III, AI/ML, Sensor Fusion, XR
Indexed description
- 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 ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Master's degree or PhD in Computer Science, or a related technical field.
- 2 years of experience with data structures and algorithms.
- Experience developing accessible technologies.
- Experience with sensor fusion, C++, or computer vision.
We work in the research-to-product space, going 0-to-1 on foundational visual-inertial fusion for odometry, localization, mapping, along with calibration, image processing for head tracking. This work powers AndroidXR devices, including GalaxyXR and upcoming devices such as Aura.
Furthermore, working alongside a group of Perception researchers and engineers, we push the boundaries of how these technologies can support delightful AR experiences, driving ideation and development cross-functionally to chart the future capabilities needed by our AR devices.
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
- Write product or system development code.
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
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