Senior Software Engineer, Head Tracking, Beam, Ai/ml

Google Google · Big Tech · Seattle, WA +3

This role focuses on defining, designing, and owning the strategy and roadmap for evaluating the performance and robustness of Google Beam's head tracking technology. The Senior Software Engineer will lead the development of evaluation infrastructure, including systems for data recording, replay, metrics computation, dashboards, and automated alerting. They will collaborate with the team to provide feedback and guide algorithm improvements, design testing scenarios using synthetic and real-world data, and work with cross-functional partners. The role requires experience in C++ and Python, building evaluation systems for real-time systems (e.g., 3D tracking, robotics, AR/VR), and experience with ML model evaluation, data pipelines, training, and deployment.

What you'd actually do

  1. Define, design, and own the end-to-end strategy and roadmap for evaluating Beam head tracking performance and robustness.
  2. Lead the development and maintenance of the evaluation infrastructure, including systems for data recording, replay, metrics computation, dashboards, and automated alerting.
  3. Collaborate closely with the team to provide feedback, guide algorithm improvements, and validate changes.
  4. Design and implement testing scenarios, including synthetic data generation and real-world data collection, to cover use cases.
  5. Work effectively with cross-functional partners, including Product Managers, UX Researchers, and other Engineering teams.

Skills

Required

  • C++
  • Python
  • building evaluation systems for real-time systems
  • 3D tracking
  • robotics
  • Augmented Reality (AR)/Virtual Reality (VR)

Nice to have

  • Computer Vision
  • Machine Learning
  • Computer Graphics
  • data structures
  • algorithms
  • performance benchmarking
  • testing infrastructure
  • data analysis/visualization tools
  • machine learning frameworks
  • data pipelines
  • training
  • deployment

What the JD emphasized

  • evaluation infrastructure
  • evaluating ML models

Other signals

  • evaluating Beam head tracking performance and robustness
  • evaluation infrastructure
  • testing scenarios
  • evaluating ML models