Software Engineer III, AI/ML, TPU Efficiency, YouTube

Google LLC; Mountain View (CA); On-site; USD 147,000 - 210,000; Full time

Benefits offered by this job

Equity

Job summary

Google is seeking a Software Engineer III, AI/ML, TPU Efficiency for YouTube in Mountain View, CA. You will own optimization of models powering the YouTube algorithm and work on model efficiency with low-precision quantization, distillation, and hardware-friendly architectures.

You will build and optimize models in the Recommendation System stack, adapt to next-gen TPU hardware, and collaborate across the stack from end-user ML models to Hardware/Software architecture.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 2 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
• 2 years of experience with accelerator hardware architectures (TPUs/GPUs).
• 2 years of experience programming in C++ and Python.
• Experience with ML frameworks such as TensorFlow, JAX, and PyTorch, or ML compilers (e.g., accelerated linear algebra (XLA)).
• Experience with ML model optimization.

Responsibilities

• Analyze ML workloads, identify compute and memory bandwidth bottlenecks, and optimize accelerator utilization to maximize compute efficiency.
• Explore, implement, and productionize algorithmic efficiency techniques, including low-precision quantization, knowledge distillation, parameter sharing, and attention optimizations.
• Optimize auxiliary serving and distributed data pipelines, including data ingestion, feature transformation, embedding lookups, and memory caching to support real-time training and inference.
• Collaborate with ML model developers and researchers to co-design hardware-friendly model architectures and deploy universal efficiency libraries.

Skills

ML infra

C++

Python

TPU/GPUs

ML frameworks

Model optimization

Education

Bachelor's degree

Tools

TensorFlow

JAX

PyTorch

XLA

Job description

Software Engineer III, AI/ML, TPU Efficiency, YouTube

Share Software Engineer III, AI/ML, TPU Efficiency, YouTube

corporate_fare YouTube place Mountain View, CA, USA

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

• Bachelor's degree or equivalent practical experience.
• 2 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
• 2 years of experience with accelerator hardware architectures (TPUs/GPUs).
• 2 years of experience programming in C++ and Python.
• Experience with ML frameworks such as TensorFlow, JAX, and PyTorch, or ML compilers (e.g., accelerated linear algebra (XLA)).
• Experience with ML model optimization.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
• Experience with debugging correctness and performance issues at all levels of the ML software stack.
• Experience with ML compilers and their internals, experience writing compiler optimization passes.

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.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
As a part of this team, you will own the optimization of the models powering the YouTube algorithm. Your work will focus on model efficiency optimization, such as low-precision quantization , knowledge distillation, parameter sharing, and designing hardware-friendly model architectures, to reduce training and serving costs while maximizing fleet utilization and value delivered to users.
You will build support and optimize new and existing models in our Recommendation System stack, including new model architectures while adapting to next-generation TPU hardware.
You will engage in model and TPU compiler co-design, with opportunities to work across the stack ranging from end-user ML models down to Hardware/Software architecture.
At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .

• Analyze ML workloads, identify compute and memory bandwidth bottlenecks, and optimize accelerator utilization to maximize compute efficiency.
• Explore, implement, and productionize algorithmic efficiency techniques, including low-precision quantization, knowledge distillation, parameter sharing, and attention optimizations.
• Optimize auxiliary serving and distributed data pipelines, including data ingestion, feature transformation, embedding lookups, and memory caching to support real-time training and inference.
• Collaborate with ML model developers and researchers to co-design hardware-friendly model architectures and deploy universal efficiency libraries.

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy , Know your rights: workplace discrimination is illegal , Belonging at Google , and How we hire .

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

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Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.