Staff Software Engineer, Performance and Kernel, Deepmind

Google Google · Big Tech · Mountain View, CA +3

Staff Software Engineer focused on performance and kernel development for AI accelerators within DeepMind's GenAI Systems. The role involves writing low-level software, identifying suitable algorithms for new hardware, building programming infrastructure, and collaborating with cross-functional teams to optimize efficiency and performance. This position operates at the intersection of hardware and software co-design for AI workloads.

What you'd actually do

  1. Write low-level performance software for a new systolic array accelerators.
  2. Identify high-value algorithms and problems that are a match to this new machines.
  3. Use that direct experience delivering production code to build programming infrastructure to ease programming on the new accelerator for the performance software engineers and general software engineers who follow.
  4. Collaborate with application, algorithm, and performance-tuning experts to codesign solutions for the best efficiency, performance, and programmability.
  5. Communicate the performance, programmability, and efficiency results to our team and to our partner teams.

Skills

Required

  • software development
  • testing and launching software products
  • power or performance modeling
  • system performance analysis
  • Linux Kernel development
  • software and hardware architecture
  • software design and architecture

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • data structures and algorithms
  • complex, matrixed organization involving cross-functional, or cross-business projects
  • technical leadership role leading project teams and setting technical direction
  • operating systems
  • networking systems
  • storage systems
  • analytics
  • machine learning
  • distributed query processing
  • deep functional flows

What the JD emphasized

  • production code
  • performance modeling
  • system performance analysis
  • Linux Kernel development
  • software and hardware architecture

Other signals

  • building programming infrastructure
  • performance optimization
  • low-level software for accelerators
  • codesign hardware and software