Distinguished Engineer, GPUs, Core ML, Borg, Spatial Flex
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Minimum qualifications:
- Bachelor's degree in Computer Science, a similar field, or equivalent practical experience
- 18 years of experience working with GPU inference optimization
- 18 years of experience in GPU performance related work
- Experience designing and developing large-scale infrastructure systems, working in a public cloud environment for enterprise users
- GPU Inference optimization experience
Preferred qualifications:
- 20 years of professional experience in GPU performance related work at all levels of the stack
- Deep understanding of modern GPU architectures memory hierarchies, and performance bottlenecks
- Ability to develop and utilize sophisticated performance models and benchmarks to guide optimization efforts and hardware roadmap decisions
- Excellent communication and people skills, with the ability to effectively collaborate with customers and internal teams
- Expertise in tailoring algorithms and ML models to exploit GPU strengths and minimize weaknesses
About the job
Google’s ML, Systems and Cloud AI (MSCA) organization builds the technical foundation behind Google’s products as well as for Google Cloud’s compute and AI/ML offerings. We manage the underlying design elements, developer platforms, product components, and infrastructure at Google. Core ML is the central machine learning platform team that provides ML software tools, services, solutions and infrastructure to all the Google product areas, including Search, Ads, Youtube, Cloud, Maps, etc. Core ML is focused on Driving ML Excellence for Google and the World. Our aim is to make it easier to perform ML experimentation, development and productionization and we work closely with Google Research and DeepMind to bring new ML models (e.g. Gemini) and innovation across the stack to market. This enables us to better meet the challenge of the rapidly evolving hardware and software space around ML.
Core ML is searching for a highly skilled and motivated Distinguished Engineer to play a leadership role in evaluating a collection of forthcoming hardware technologies for optimizing our offerings in this space. Advanced evaluation of GPU offerings and working closely with the internal TPU offerings to properly position and advocate for both cloud and internal customer GPU use-cases. This role will also directly contribute across Cloud and other product areas in creating the right infrastructure to access our ML Systems.
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 $349,000-$485,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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 efforts to optimize machine learning models for speed, memory efficiency, and accuracy through experimentation with different architectures, hyperparameters, and optimization techniques.
- Translate customer requirements into technical solutions by working closely with them to understand their needs. This includes presenting technical findings and recommendations.
- Guide and inspire junior engineers by leading by example, sharing your expertise, and providing guidance on best practices.
- Identify bottlenecks and areas for improvement by developing and utilizing performance analysis tools.
- Influence architecture of Google’s internal and external software architecture, as well as vendor and internal hardware roadmaps.
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