Principal Staff Software Engineer, AI and Data Infrastructure
Job Summary
Job Description
- 8+ years software development experience
- 7+ years leading technical projects & ML design
- 5+ years experience with speech/audio or reinforcement learning
- 5+ years in design, architecture, testing & launching software
- Proficiency in Python, data structures & algorithms
- Master's/PhD in related field
- 5+ years technical leadership
- Generative AI (GenAI) model experience
- Knowledge of ML systems for production
- Ability to define data use-cases and balance trade-offs
9 skills required
Job Details
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development and with data structures/algorithms in Python.
- 7 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).
- 5 years of experience with speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making) or ML infrastructure, or related ML field.
- 5 years of experience with design and architecture; and testing/launching software products.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 5 years of experience in a technical leadership role leading project teams and setting technical direction.
- Knowledge of Generative AI (GenAI) model development fine-tuning and model adaptation.
- Knowledge of ML systems and infrastructure for production with customers and engineers.
- Ability to develop a use-case specific definition for the data and pragmatically balance trade offs for research, privacy, and product usage.
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.
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
- Collaborate with Google Cloud and Google DeepMind teams to ensure the Gemini models are improved rapidly based on customer feedback.
- Work across teams and organizations to navigate technical ambiguity and bring clarity to the engineering work. This requires active scoping and driving progress with detailed attention to technical details while aligning it with a big picture strategy.
- Balance architectural and design responsibilities with active participation in coding to provide technical leadership and accelerate development cycles, ensuring seamless integration and rapid iteration across teams.
- Develop and implement systems-based solutions to problems, balancing planning with rapid iteration to achieve timely results.
- Drive technical project strategy, lead Machine Learning (ML) infrastructure optimization, and oversee the design and implementation of solutions across multiple specialized ML areas.
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About The Company
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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