Staff Software Engineer, AI/ML, Google Workspace
Job Summary
Job Description
- 5+ years experience in ML (speech/audio, reinforcement learning, infrastructure)
- 5+ years experience leading ML design & optimizing infrastructure
- 5+ years experience testing and launching software products
- 3+ years experience in software design and architecture
- Master's/PhD in Engineering or related field
- 3+ years technical leadership experience
- 3+ years experience in cross-functional projects
- 8+ years experience in software development and data structures/algorithms
- bonus
- equity
- benefits
9 skills required
Job Details
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience testing, and launching software products.
- 3 years of experience with software design and architecture.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in an organization involving cross-functional, or cross-business projects.
- 8 years of experience in software development, and with data structures/algorithms.
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 web is what you make of it and our team is helping the world make more of the web. From open-source pros to user-experience extraordinaires, we develop products that help users connect, communicate and collaborate with others. Our consumer products and cloud platforms are giving millions of users at homes, businesses, universities and nonprofits around the world the tools that shape their web experience -- and changing the way they think about computing.
The US base salary range for this full-time position is $197,000-$291,000 + 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 .
Responsibilities
- Design, develop, test, deploy, maintain, and enhance large scale software solutions.
- Provide technical leadership on high-impact 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.
- Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
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