Software Engineer, Accelerators Software/Hardware Co design, DeepMind

We are building a next-generation compute platform for cutting-edge AI inference workloads, with an emphasis on innovative memory technologies, hardware acceleration, and software/hardware codesign.

At Google DeepMind you’ll be joining our team of scientists and engineers who are directly impacting the future of machine learning for Google and industry.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

• Collaborate with product teams to design, implement, and prototype novel software algorithms.
• Oversee software/hardware codesign, kernel optimization, performance benchmarking and optimization.
• Collect insights to influence future software and hardware design and the technology roadmap.

Minimum qualifications:

• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
• Experience in software development using Python and C++.
• Experience with Machine Learning (ML) hardware accelerators and ML inference software.

Preferred qualifications:

• PhD in Computer Engineering, Computer Science, or a related field.
• 2 years of experience with hardware accelerators, including architecture, software/hardware co-design, and performance optimization.
• 2 years of experience with collaborative, hyper-scaler software development and engineering best practices.
• Experience scoping, planning, and executing projects towards team goals in ambiguous environments.