Machine Learning Staff Software Engineer, Pixel Camera

• Design and implement the real-time on-device machine learning foundation that turns raw camera and sensor input into structured signals driving immediate application behavior.
• Drive the optimization of outlook models for NPU, GPU, and DSP execution, including quantization, hardware-aware architecture design, and custom kernel development, in partnership with the Tensor silicon and compiler teams.
• Own models end-to-end, from prototype through deployment on hundreds of millions of devices — training pipelines in JAX and TensorFlow, C++ integration into the camera pipeline, and long-term maintainability.
• Partner with product, UX, software, and hardware teams to define the requirements for next-generation interactive features, and translate roadmap goals into designs achievable within device constraints.
• Establish best practices for machine learning development, deployment, and evaluation. Define how model quality is measured; and contribute to the long-term technology roadmap.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 8 years of experience with software design and architecture.
• Experience with C, C++, machine learning, and embedded systems.
• Experience with machine learning algorithms.
• Experience with machine learning architecture.
• Experience with machine learning research.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• Experience taking machine learning models from research prototype to production on mobile or embedded devices, and owning them after launch.
• Experience optimizing models for on-device accelerators through quantization, hardware-aware architecture design, or custom kernels.
• Experience building and training models in JAX/TensorFlow.

• Bachelor's degree or equivalent practical experience.
  
• 8 years of experience with software design and architecture.
• Experience with C, C++, machine learning, and embedded systems.
• Experience with machine learning algorithms.
• Experience with machine learning architecture.
• Experience with machine learning research.