Minimum qualifications:

• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.

• 8 years of experience with software development in one or more programming languages, and with data structures/algorithms.

• 5 years of experience in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.

• 5 years of experience with computer architecture and performance.

Preferred qualifications:

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

• 5 years of experience working in a complex, matrixed organization.

• 5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), or applied Machine Learning (e.g., deep learning, natural language processing).

• Experience with hardware design, architecture, and engineering.

• Experience with chip design, simulation, or benchmarking.

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

The ML Accelerators Roadmap and Co-Design team plays a critical role in exploring silicon design for future, pre-silicon chip designs, as well as enabling others to more effectively tune for existing hardware.

We collaborate with researchers to understand the future of ML workloads and translate those insights into potential hardware architectures. Through advanced simulations and performance projections, we evaluate and refine chip design concepts in partnership with chip implementation and infrastructure engineers, paving the way for next generation efficient and powerful ML hardware.

Our explorations drive a dynamic cycle of co-design, where future hardware possibilities influence software and model development, and vice versa.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $237,000-$337,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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.

• Lead the exploration of the silicon design space for future machine learning hardware, collaborating with researchers to understand future workload requirements and support hardware engineering teams in translating them into potential chip architectures.

• Develop and implement robust processes, infrastructure, and analysis methodologies for evaluating machine learning workloads on distributed and heterogeneous systems.

• Utilize advanced simulation and modeling techniques to evaluate and refine pre-silicon chip designs, identifying avenues for future hardware development.

• Drive efficiency improvements in machine learning workloads through co-designed algorithmic and system-level techniques and maintain objectivity and analytical precision in assessing performance of different design parameters for chips, systems, and machine learning model architectures.

• Lead and inspire a team of engineers, fostering a culture of collaboration, innovation, and technical excellence.

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/OFCCPEEOPost.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.