Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience with ML design and architecture; and testing/launching software products.
- 5 years of experience in software development.
- 5 years of experience in leading technical project strategy, machine learning (ML) design, and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- Experience in full-stack development languages and technologies (e.g., Python, Angular or Typescript).
Preferred qualifications:
- 5 years of experience in a technical leadership role leading project teams and setting technical direction.
- 5 years of experience with data structures/algorithms.
- Experience in Data Science or adjacent work or on SWE/DS collaborations and with machine learning/GenAI evaluation or related fields (e.g., model training or evaluation processes, TPU optimization).
- Experience working with co-owned systems or closely integrated systems maintained across team boundaries and working across time zones and maintaining productivity with limited communication bandwidth.
- Knowledge of software engineering principles, design patterns, and architectural best practices.
- Excellent communication skills; with ability to articulate technical concepts.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
The Evaluation Platform team in AI Data accelerates Google's AI development by providing evaluation tools and infrastructure for LLM models and AI agents. Teams like Bard, Magi, Vertex AI, Workspace, and Gemini One Recipe use our infrastructure. Our Everest core platform manages over 100,000 evals weekly for more than 100 customer teams across eight product areas. EvalHub, our primary visualization system, is the central hub for managing and analyzing eval results, serving as the definitive source for Google's key model development. It empowers researchers and engineers to make faster, better-informed decisions, advancing Gemini and Google's AI ecosystem.
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
- Design, develop, test, and maintain features across the full stack for EvalHub and related systems such as Gemini Data Studio from our Angular/TypeScript front-end to our Python back-end services.
- Collaborate across organisations and locations to develop and execute on the roadmap for eval quality efforts, enhance the end-to-end generative AI and Agent Eval UX by managing key issues in data visualization, debugging UI, and item visualization for eval data set and eval quality metrics.
- Lead the design and implementation of solutions in specialized machine learning (ML) areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
- Provide technical leadership on projects. Manage project priorities, deadlines, and deliverables.
- Facilitate alignment and clarity across teams on goals, outcomes and timelines. Influence and coach a distributed team of engineers.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also
Google's EEO Policy and
EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our
Accommodations for Applicants form.
