Staff Software Engineer, Ads Safety

Minimum qualifications:

• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
• 8 years of software development experience, with designing and shipping large-scale, distributed systems.
• Experience with ML serving platforms or managing ML infrastructure at a large scale.
• Experience in at least one modern programming language such as C++, Java, Python, or Go.
• Experience in a leadership role with direct reports, managing and mentoring junior engineers while maintaining individual contributor responsibilities.

Preferred qualifications:

• Master's degree or PhD in Computer Science or a related technical field.
• Experience with large-scale inference for Large Language Models (LLMs) and other modern AI techniques.
• Experience in optimizing performance for machine learning workloads on specialized hardware like TPUs, GPUs, and CPUs.
• Experience solving ambiguous and technically challenging problems, identifying root causes, and implementing systemic solutions.

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.

Within Ads Safety, this team provides serving infrastructure for classifiers of all types, including Traditional ML Models, Rules, large language models (LLMs). The team primary focus is to support high scalability, reliability, efficiency, optimal utilization of machine resources, extensibility, seamlessly supporting a variety of classifiers, classification backends and classification scenarios.

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.

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 benefits at Google.

Responsibilities

• Serve as the primary technical leader for the team, define the long-term technical goals for our serving infrastructure and to execute on that by managing the most complex, ambiguous technical issues. Design, implement, and launch highly reliable, low-latency, and fault-tolerant systems capable of managing massive inference traffic.
• Manage a small team of software engineers, fostering their career growth and technical skills.
• Advocate system reliability, scalability, and efficiency.
• Partner with peer teams to influence the broader Ads Safety machine learning (ML) Infrastructure strategy and technical roadmap.
• Act as the key individual contributor on critical projects, setting the technical bar and driving them from initial concept to launch.