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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.