Google's software engineers develop next-generation technologies that change how billions of users connect, explore, and interact with information. Products need to handle information at massive scale, and extend well beyond web search. The ML performance team needs to drive production optimizations for GenAI in the algorithmic efficiency space, survey emerging industry solutions, and contribute to research and product development.
Requirements
- 7 years of experience leading technical project strategy, ML design, and working with industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning)
- 5 years of experience with design, software architecture, and testing/launching software products
- 5 years of experience with one or more of the following: language models, image generation, reinforcement learning
- 5 years of experience with ML infrastructure, opportunity analysis (rooflines, headroom/bridge analysis)
- 8 years of experience with data structures/algorithms
- Experience in ML accelerator performance and computer architecture
- Experience using data to identify systemic issues, form hypotheses, and develop technical proposals with excellent problem-solving skills
Responsibilities
- drive production optimizations for GenAI in the algorithmic efficiency space
- survey emerging industry solutions
- contribute to research and product development
- drive the productization of solutions initially tested out in white-glove engagements, ensuring their scalability, reusability, automation, and toil reduction
- operate at the intersection of technical analysis, cross-organizational strategy, and direct execution
- Explore innovations in third-party/ third-party/Open Source Software (OSS) and ML literature to discover new algorithmic efficiency initiatives worthy of prototyping
- Lead headroom analyses and time-boxed POCs to assess the new algorithmic opportunity’s viability
Other
- Provide technical leadership on projects.
- Influence and coach a distributed team of engineers.
- Facilitate alignment and clarity across teams on goals, outcomes, and timelines.
- Manage project priorities, deadlines, and deliverables.
- Create research-to-production roadmaps, driving innovation.
