Google
Engineering Manager, DSC AI Inference Platform
Found: Today
Engineering Manager, DSC AI Inference Platform
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.
The Distributed Cloud (DSC) AI Inference Platform team operates at the critical intersection of Large Language Models (LLMs) and high-performance computing. Our mission is to engineer the future of AI serving infrastructure, driving foundational improvements in efficiency, latency, and throughput. We develop innovative solutions, including disaggregated serving architectures, and build the essential tools to analyze and optimize LLM performance on cutting-edge GPU platforms. Our work directly enables Google to deploy and scale state-of-the-art AI models (like Gemini) effectively and efficiently across Google's global infrastructure, products, and Cloud.
The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $301000 (USD) + 20% bonus target + equity + benefitsLearn more about benefits at Google.Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience programming in C++ or Python.
- 5 years of experience optimizing, profiling, and scaling production-grade systems on GPU accelerators or specialized AI hardware.
- 5 years of experience directly managing and leading engineering teams focused on machine learning infrastructure, AI platforms, or high-performance distributed computing systems.
- 5 years of experience in a people management or team leadership role.
- 3 years of experience managing engineering organizations across multi-team infrastructure dependencies.
Preferred qualifications:
- Master's degree or PhD degree in Computer Science or a related technical field.
- 5 years of experience working in a complex, matrixed organization.
- 4 years of experience implementing advanced LLM serving architectures and optimization techniques, such as disaggregated serving, continuous batching, or specialized compiler technologies (e.g., XLA).
- 3 years of experience utilizing deep-dive ML profiling tools (e.g., Nsight, xprof) to troubleshoot and resolve low-level bottlenecks within major frameworks like JAX, PyTorch, or TensorFlow.
Responsibilities
- People Management and Talent Development: Lead, mentor, and grow a high-performing team of systems and ML engineers. Drive a culture of excellence, psychological safety, and continuous learning. Guide career paths, define OKRs, and conduct performance evaluations.
- Strategic and Technical Roadmap: Define the technical goal and strategy for enhancing the LLM serving stack, focusing on performance, scalability, and resource efficiency.
- Architectural Leadership: Drive the design and implementation of advanced serving architectures, including disaggregated serving, to optimize resource utilization and latency.
- *Infrastructure Oversight:* Oversee the building and maintenance of critical infrastructure and tooling for in-depth performance analysis, profiling, and benchmarking of LLM models on GPU accelerators.
- Cross-Functional Collaboration: Partner closely with Research, SRE, Product, and Core GPU library teams to optimize and deploy LLMs in production globally. Align team efforts with broader organizational AI priorities.
