Senior Engineering Manager, ML Performance
Job Summary
Job Description
- 8+ years software development experience
- 5+ years technical leadership
- 5+ years people management
- Experience with performance analysis
- LLM performance optimization
- TensorFlow/JAX TPU experience
- Master's/PhD in CS
- Experience in a matrixed organization
- Compiler optimization experience
- Numeric/algorithmic optimization experience
- Experience with machine learning systems
- Bonus
- Equity
- Benefits
6 skills required
Job Details
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++).
- 5 years of experience in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.
- Experience with performance analysis.
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience working in a matrixed organization.
- Experience working on compiler optimizations or related fields.
- Experience with numeric and algorithmic optimization like quantization, sparsity, or model architecture improvement.
- Experience in machine learning systems (e.g., background theory, TensorFlow, or other ML tools).
About the job
Like Google's own ambitions, the work of a Software Engineer goes way 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.
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 .
Responsibilities
- Focus on LLM performance analysis and optimizations for partner teams including Google Gemini, Search Magi, Cloud LLM APIs, etc.
- Identify and maintain LLM training and serving benchmarks and use them to identify performance opportunities and drive TensorFlow/JAX TPU out-of-the-box performance toward state-of-the-art.
- Explore numeric and algorithmic optimizations, new ML model architecture/optimizer/training techniques to solve ML tasks more efficiently, and new techniques to reduce the label/unlabeled ML data needed to train a model to target accuracy.
- Engage with various Google product teams to solve their LLM performance challenges, including onboarding new LLM models and products on Google’s TPU hardware and enabling LLMs to train efficiently on a very large scale (i.e., thousands of TPUs).
- Analyze performance and efficiency metrics to identify bottlenecks, design, and implement solutions at Google fleet-wide scale.
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A problem isn't truly solved until it's solved for all. Googlers build products that help create opportunities for everyone, whether down the street or across the globe. Bring your insight, imagination and a healthy disregard for the impossible. Bring everything that makes you unique. Together, we can build for everyone.
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