Senior Staff Software Engineer, Data and Model Insights and Infrastructure

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 7 years of experience leading technical project strategy, ML design, and working with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 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 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.
• 8 years of experience with data structures and algorithms.
• 5 years of experience in a technical leadership role leading project teams and setting technical direction.
• 5 years of experience with design and architecture; and testing/launching software products.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• 2 years of demonstrated track record of technical innovation in system/algorithm/ML efficiency or related fields.

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 AI Data organization is responsible for democratizing high-quality AI/ML data assets and infrastructure to enable Google to rapidly and iteratively deliver safe, innovative, and impactful product experiences powered by exceptional AI models. We build scalable and automated infrastructure to manage ML assets at Google from development to launch with full traceability and auditability, without compromising dev velocity.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Understand the design and architecture of highly complex ML pipelines and distributed systems, with a view towards making them more resource efficient.
• Drive the adoption of next-generation AI/ML techniques to solve ambiguous, high-impact business problems, moving from conceptual prototypes to global production.
• Set the "gold standard" for code quality, testing, and deployment within the team, performing deep-dive architectural reviews and optimizing system performance.
• Mentor senior and mid-level engineers, fostering a culture of technical growth, and represent the team in high-level technical discussions across the broader organization.
• Partner with researchers to operationalize models and work with product stakeholders to align technical roadmaps with user needs.