Senior GPU Performance Software Engineer, AI/ML

Google; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. This role sits on the ML-Omega team, optimizing GPU systems for diverse ML workloads and benchmarking for Google’s internal workloads.

We strive for maximum efficiency and scalable performance, and seek engineers who can contribute across the full stack while collaborating with product teams such as DeepMind to tackle ML model

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 5 years of software development experience.
• 5 years of data structures and algorithms experience.
• 5 years of ML/AI/NLP experience.
• 3 years of software testing/maintenance/launching; 1 year of design/architecture experience.

Responsibilities

• Identify and maintain Large Language Model (LLM) training and serving benchmarks that are representative of Google production, industry, and the ML community, using them to identify performance opportunities, drive Accelerated Linear Algebra (XLA) GPU/Triton performance, and guide XLA releases.
• Engage with Google product teams like DeepMind to solve their ML model performance problems, such as onboarding new LLM models and products on GPU hardware and enabling LLMs to train and serve efficiently at a very large scale.
• Run architecture-level simulations on GPU designs and perform roofline analysis to guide internal teams.
• Analyze performance and efficiency metrics to identify bottlenecks, as well as design and implement solutions at Google fleet-wide scale.
• Run performance benchmarks on GPU hardware using internal and external tools.

Skills

Software development

Data structures & algorithms

Machine learning

Software testing

Software architecture

Technical leadership

Hardware co-design

High-performance computing

GPU programming

Compiler optimizations

Education

Bachelor’s degree

Master’s or PhD preferred

Tools

CUDA

Triton kernels

XLA

Compiler design tools

Job description

In most instances, this position requires in-person interviews as part of the hiring process.

Minimum qualifications

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages.
• 5 years of experience with data structures and algorithms.
• 5 years of experience with machine learning algorithms and tools, artificial intelligence, deep learning, or natural language processing.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

Preferred qualifications

• Master's degree or PhD in Computer Science or a related technical field.
• 1 year of experience in a technical leadership role.
• Experience with hardware/compiler co-design or high-performance computing (HPC).
• Experience in performance analysis and debugging, improving performance of single-node or multi-node (distributed) systems.
• Experience in GPU programming using CUDA or Triton kernels (or Palace/Mosaic kernels).
• Background in Compiler optimizations or related fields would also be beneficial.

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.

The Machine Learning (ML)-Omega team is responsible for optimizing, modeling, and evaluating GPU systems for comparative analysis and benchmarking for Google’s internal ML workloads. We strive for extracting maximum efficiency in Google’s GPU fleet, evaluating current and future ML workloads to guide decision-making for the Cloud hardware teams.

Responsibilities

• Identify and maintain Large Language Model (LLM) training and serving benchmarks that are representative of Google production, industry, and the ML community, using them to identify performance opportunities, drive Accelerated Linear Algebra (XLA) Graphics Processing Unit (GPU)/Triton performance, and guide XLA releases.
• Engage with Google product teams like DeepMind to solve their ML model performance problems, such as onboarding new LLM models and products on GPU hardware and enabling LLMs to train and serve efficiently at a very large scale.
• Run architecture-level simulations on GPU designs and perform roofline analysis to guide internal teams.
• Analyze performance and efficiency metrics to identify bottlenecks, as well as design and implement solutions at Google fleet-wide scale.
• Run performance benchmarks on GPU hardware using internal and external tools.

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.