Senior Software Engineer, Generative AI, Ads Safety

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development, applied ML, autonomous systems, and large-scale systems design.
• 3 years of experience with end-to-end machine learning (e.g., model building, model deployment, model evaluation, optimization, data processing, and debugging).
• 3 years of experience with one or more of the following machine learning specializations: Multi-Modal LLMs, Computer Vision, NLP, Reinforcement Learning.
• 1 year of experience with GenAI techniques (e.g., Agentic Workflows, RAG, ICL, or RSI).
  

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 5 years of experience with data structures and algorithms.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
  
• 1 year of experience in a technical leadership role.
• Experience developing accessible technologies.

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 Accelerator (MLA) team within Ads Content Safety organization makes tangible impact on user trust and Google's ads ecosystem. This ML team designs, builds, and deploys robust, automated, and highly responsive ML solutions for our next-generation multi-modal content understanding systems.

You will advocate for ML excellence as you contribute to our boldest architectural shifts. By utilizing cutting-edge GenAI techniques (Agentic Workflows, RAG, ICL, RSI), you will drastically slash policy enforcement latency from days to minutes and remove human bottlenecks from the ML lifecycle. You will design and build Multi-Modal LLM teacher models for video understanding for ads at scale. Your work will directly empower our policy enforcement, safeguard billions in revenue, and establish a new industry standard for high-velocity, autonomous AI systems.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Responsibilities:

• Write and test product or system development code.
• Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
• Engineer a sophisticated toolkit for the automated model training lifecycle, deploying AI agents to automate model evaluation and prompt optimization, effectively removing human intervention from the critical path of model development.
  
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• Design and implement end-to-end (E2E) ML architectures for Ads Safety, including developing robust capabilities for automated model refreshing, high-scale data pipelining/funneling, and proactive signal integration to improve overall detection quality.