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 Tensor Processing Unit Supply Chain and New Product Introduction team operates at the intersection of machine learning, compilers, and Tensor Processing Unit hardware.
In this role, you will collaborate with teams across hardware and software, and enable Google to have the industry's most performant machine learning chip (Tensor Processing Unit) at the hands of machine learning modeling teams. The majority of machine learning at Google (and for Cloud customers) flows through tools that the Tensor Processing Unit backend and New Product Introduction team is creating, so impact is both immediate and massive.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) + 15% bonus target + bonus + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Contribute to the Tensor Processing Unit SparseCore compiler for a processor designed to accelerate machine learning workloads.
- Architect and compile implementations of operations at a distributed scale.
- Work with users of Tensor Processing Units to improve performance and hardware designers to co-design future processors, and investigate high-level representations to program distributed, and heterogeneous systems.
- Enable a processor to accelerate machine learning workloads.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
- 1 year of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures and algorithms.
- Experience developing accessible technologies.