Engineer III, AI/ML, Online Calibration, 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.
- 2 years of experience developing in C++ or Python.
- 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.
- Knowledge of Inertial Measurement Unit (IMU), camera, and magnetometer sensor models and calibration techniques.
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 Machine Learning (ML) areas, utilize ML infrastructure, and contribute to model optimization and data processing.
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