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 Content Safety data platform team manages the critical infrastructure and tools that enables the full AI innovation cycle, from data set creation to model development to product measurements.

In this role, you will empower developers to validate ideas at high velocity by providing early access to custom models and expansive support for all input modalities. You will eliminate redundant effort by providing high-quality, various datasets and frameworks, making systematic benchmarking seamless and repeatable. You will architect and mature the core stacks to support critical GenAI workloads with enterprise grade reliability and efficiency. You will streamline the upgrade path for deployed features, automating the transition to newer model versions and managing complex signal backfills so teams can inherit quality improvements effortlessly.

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 $174,000-$252,000 + 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

• Drive technical strategies to develop and expand Machine Learning (ML) data platform adoption.

• Navigate high ambiguity by building greenfield prototypes and answer critical-path technical questions to drive leadership conviction and unblock major investments.

• Support the growth of junior engineers through code or design reviews and modeling sound trade-offs, while partnering to shape long-term technical strategy.

• Proactively identify system risks, manage technical debt, and shield the team from distractions.

• Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.

Minimum qualifications

• Bachelor’s degree or equivalent practical experience.

• 5 years of experience programming in C++.

• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

• 3 years of experience developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.

• Experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications

• Master's degree or PhD in Computer Science or related technical field.

• 5 years of experience with data structures and algorithms.

• 1 year of experience in a technical leadership role.

• Experience with SQL, and distributed computing platforms (Hadoop, App Engine, etc.).