Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development and with data structures/algorithms (e.g., C++ or Python).
- 5 years of experience with Machine Learning (ML) design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- Experience working with GPUs.
- Experience in a technical leadership role leading project teams and setting technical direction.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- Experience with compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc.).
- Expertise in tailoring algorithms and ML models to exploit GPU strengths and minimize weaknesses.
- Knowledge of low-level GPU programming (CUDA, OpenCL, etc.) and performance tuning techniques.
- Understanding of modern GPU architectures, memory hierarchies, and performance bottlenecks.
- Ability to develop and utilize sophisticated performance models and benchmarks to guide optimization efforts and hardware roadmap decisions.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
In recognition of hardware as a strength, Google’s Core Machine Learning (ML) organization is heavily invested in growing a powerhouse team of GPU experts, and we invite you to be at its vanguard! This is your opportunity to move beyond incremental improvements and architect truly transformative solutions, shaping the future of AI and accelerated computing for Google and the world.
While known for pioneering work with TPUs, GPUs are an equally vital and rapidly expanding frontier within Google's machine learning infrastructure. GPUs are indispensable to Google’s ever-evolving landscape for strategic, pragmatic, and performance-driven reasons — ensuring top performance for our ML models, adapting to ML workloads, achieving results, and influencing next-generation GPU architectures via strategic partnerships.
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
- Build optimizations that improve benchmarks, but also power Google's most critical products and services, impacting billions of users worldwide and driving significant cloud business.
- Shape the entire GPU software stack through influencing model design, optimizing low-level kernels and compilers (OpenXLA, JAX, Triton), and bridging the gap between model developers and hardware for optimal co-design and performance.
- Resolve the most challenging performance bottlenecks and explore groundbreaking optimization techniques through Google’s unparalleled access to the latest generation of GPUs, tooling, and a decade of experience building AI accelerators.
- Collaborate with experts in ML, compiler design, and systems architecture through internal and external partnerships, as well as open-source projects.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also
Google's EEO Policy and
EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our
Accommodations for Applicants form.
