Software Engineering Manager, ML Metrics
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
- 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 3 years of experience in a technical leadership role; overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.
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, and/or cross-business projects.
- Experience with backend programming (C++) and data analytics (SQL) for development of instrumentation and data pipelines.
- Experience in Compilers, Computer Architecture, Distributed Systems or related fields.
- Experience in System Optimization techniques
- Experience with FlumeC++.
About the job
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.
In this role, you will work closely with other teams within ML Performance to evaluate and deploy techniques that improve the performance and efficiency of ML training and ML inference workloads. You will work with the ML performance tools team and other Core ML teams to design and collect necessary metrics across the software and hardware stack. You will also work with key ML stack stakeholders throughout Google.
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 $189,000-$284,000 + 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.
Responsibilities
- 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.
- Measure and report the efficiency of the ML fleet, generate and collect metrics that help identify optimization opportunities, and drive improvements via changes to Core ML products and services.
- Design, guide and vet systems designs within the scope of the broader area, and write product or system development code to solve ambiguous problems.
- Measure and report the fleetwide adoption of Core ML products and services.
- Provide data driven feedback to ML job owners, product area resource planners, fleet resource planners, and ML, Systems, and Cloud AI leaders.
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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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