Machine Learning Algorithm Engineer, Silicon

Google is seeking a Machine Learning Algorithm Engineer to join their Silicon team, focusing on developing cutting-edge custom silicon solutions that power Google's direct-to-consumer products. This role combines advanced machine learning with hardware optimization, specifically in the domain of camera and video processing.

The position requires deep expertise in both computer architecture and machine learning, particularly in image processing and computational photography. You'll be working at the intersection of ML algorithms and hardware design, developing solutions that need to balance performance, efficiency, and integration requirements.

The role involves collaborating with multiple teams including Hardware Architects, Research, Image Quality, and Software teams to develop new Video ML algorithms and determine optimal software/hardware partitioning. You'll be responsible for researching and implementing state-of-the-art camera Video ML algorithms that enable new imaging use cases.

This is an excellent opportunity for someone passionate about both machine learning and hardware optimization. You'll be working on products that impact millions of users worldwide, with access to Google's extensive resources and cutting-edge technology. The position offers competitive compensation ($156,000-$229,000 base salary) plus bonus, equity, and comprehensive benefits.

The ideal candidate will have at least 5 years of experience with computer architecture concepts and machine learning-based image processing, along with strong programming skills in C/C++ or Python. Knowledge of machine learning frameworks like TensorFlow or PyTorch is essential. Advanced degree holders with expertise in computer architecture and experience in generative AI models for image/video processing will be particularly competitive.

Working at Google, you'll be part of a team that pushes boundaries in AI and hardware integration, with the opportunity to shape the next generation of Google's hardware experiences. The role offers the chance to work on challenging technical problems while contributing to products that have real-world impact.