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Minimum qualifications:

+ Bachelor’s degree or equivalent practical experience.

+ 8 years of experience in software development, and with data structures/algorithms.

+ 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

+ 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

+ 5 years of experience with one or more of the following: embedded system or lower level Software stacks (e.g., networking, storage, etc.), ML infrastructure, Compilers (e.g. XLA), or specialization in another ML field.

Preferred qualifications:

+ Master’s degree or PhD in Engineering, Computer Science, or a related technical field.

+ 3 years of experience working in a matrixed organization involving cross-functional, or cross-business projects.

+ 3 years of experience in a technical leadership role leading project teams and setting technical direction.

+ Experience with performance analysis and optimization such as system architecture, performance modeling, or related.

+ Experience with machine learning systems (e.g, background theory, TensorFlow, and other tools).

+ Experience with compiler optimizations or related fields.

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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

In this role, you will be responsible for extracting maximum efficiency for machine learning and AI workloads. You will lead Google ML performance to state-of-the-art using deep fleet-scale and benchmark analysis and out-of-the-box auto-optimizations

You will focus on performance analysis to identify performance opportunities in Google production and research ML workloads and land optimizations to the entire fleet. Your work demonstrates cutting edge ML performance on largest scale and latest accelerators at MLPerf competition.

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.

The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google (https://careers.google.com/benefits/) .

+ Identify and maintain LLM training and serving benchmarks that are representative to Google production, used by industry and ML community to identify performance opportunities and drive TensorFlow/JAX TPU performance.

+ Work on scaling numeric and algorithmic optimizations to Google products and ML models. For example, quantization, sparsity, and other model compression techniques, new ML model architecture/optimizer/training techniques to solve ML tasks more efficiently, or new techniques to reduce the label/unlabeled ML data needed to train a model to target accuracy.

+ Collaborate cross-functionally to solve LLM performance problems. For example, on-boarding new LLM models and products on Google new TPU hardware, enabling LLMs to train efficiently on a very large scale (i.e., thousands of TPUs), etc.

+ Analyze performance and efficiency metrics to identify bottlenecks, design, and implement solutions at scale.

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 https://careers.google.com/eeo/ and https://careers.google.com/jobs/dist/legal/OFCCP_EEO_Post.pdf If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form: https://goo.gl/forms/aBt6Pu71i1kzpLHe2.