Description

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience with software development in C and Python programming languages.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
  
• 1 year of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 7 years of experience in software engineering, applied ML, autonomous systems, and large-scale systems design.
• 5 years of experience with data structures/algorithms, and 1 year of experience in a technical leadership role.
• 2 years of experience with Generative AI Agents.
• 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.

In this role, you will enhance and migrate models to Gemini-based architectures to improve development velocity. You will simplify the training process by transitioning to In-Context Learning (ICL) on base Gemini models, and develop a toolkit of agentic solutions to automate the Machine Learning (ML) life-cycle.

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

• Write and test product or system development code.
• Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
• Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Participate the transition from legacy FLAMeD fine-tuned models to In-Context Learning (ICL) on Base Gemini models, ensuring optimal performance while optimizing for inference cost and throughput.
• Design and roll out the Per Head Deep Update (PHDU) architecture across models and replace monolithic models with a modular system to enable independent, parallel policy updates. Engineer a toolkit for the automated model training life-cycle and deploy AI agents to automate model evaluation and prompt optimization.

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Requisition #: 116600427088093894

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