Software Engineer, Quantization and Algorithmic Efficiency

Minimum qualifications

• Bachelor’s degree or equivalent practical experience.

• 8 years of experience in software development.

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

• 3 years of experience leading technical project strategy, ML design, and working with industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

• 3 years of experience with ML infrastructure, optimization opportunity analysis (e.g., rooflines, headroom/bridge analysis).

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

• 8 years of experience with data structures/algorithms.

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

• Experience in ML accelerator performance and computer architecture.

• Experience in presenting technical information and recommendations to executive leadership.

• Experience using data to identify systemic issues, form hypotheses, and develop technical proposals with excellent problem-solving skills.

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 this role, you will be working as a lead on the Machine Learning Performance team, in collaboration with the compiler, runtime, customer engineering, tooling, and customer teams, including Search, Vertex, Google Deep Mind, and Cloud. You will emphasize innovation and will drive production optimizations for GenAI in the algorithmic efficiency space, survey emerging industry solutions, and contribute to research and product development.

You will drive the productization of solutions initially tested out in white-glove engagements, ensuring their scalability, reusability, automation, and toil reduction. You will operate at the intersection of technical analysis, cross-organizational strategy, and execution.

The ML, Systems, and Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, You Tube, 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.

• Design, develop, test, deploy, maintain, and enhance large scale software solutions. Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
• Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
• Explore innovations in third-party/Open Source Software (OSS) and ML literature to discover new algorithmic efficiency initiatives worthy of prototyping.
• Lead analyses and time‑boxed POCs to assess the new algorithmic opportunity’s viability.
• Create research‑to‑production roadmaps, driving innovation. Collaborate with the compiler, runtime, serving and post‑training stack leads on cross‑functional opportunities which remove bottlenecks to algorithmic efficiency.

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 and If you have a disability or special need that requires accommodation, please let us know by completing our .

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