Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Kirkland, WA, USA; New York, NY, USA; Reston, VA, USA; Seattle, WA, USA.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 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 ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience testing, and launching software products.
• 5 years of experience with software design and architecture.

Preferred qualifications:

• 10 years of experience in software engineering roles.
• 5 years of experience in designing, building, and productionizing AI/ML systems, including experience with Large Language Models (LLMs) or AI agent development, and familiarity with common AI/ML libraries/frameworks (e.g., TensorFlow, PyTorch, JAX, scikit-learn, LangChain).
• Experience leading the architectural design and development of large-scale distributed systems, including experience with microservices architecture or containerization technologies (e.g., Docker, Kubernetes).
• Experience with server-side programming languages (e.g., Python, Go), advanced SQL, cloud platforms (e.g., Google Cloud Platform), and building/managing scalable data pipelines (ETL/ELT).

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.

As a part of this team, you will be at the forefront of transforming Governance, Risk, and Compliance (GRC) for Google Cloud and Technical Infrastructure (TI). You will build an intelligent, AI-powered platform to automate and scale critical GRC functions. Your mission is to engineer innovative solutions that leverage Artificial Intelligence and Machine Learning to provide continuous assurance, enforce privacy, streamline audits, enhance reporting, and power real-time Continuous Controls Monitoring (CCM), ultimately safeguarding Google's security and compliance posture.
The US base salary range for this full-time position is $207,000-$300,000 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

• Define and drive the technical strategy and architecture for a scalable platform supporting AI-driven Governance, Risk, and Compliance (GRC) functions, while owning the end-to-end design and implementation of significant platform components.
• Lead the development and implementation of novel AI/ML models and algorithms to automate and scale Continuous Controls Monitoring (CCM), privacy control enforcement, audit processes, risk reporting, and other GRC workflows, address ambiguous problems and making key design trade-offs.
• Oversee processes for assessing data signal quality, designing and leading the engineering of scalable data pipelines to ingest, process, and validate various datasets for training AI models.
• Build and disseminate a deep understanding of control objectives, scope, implementation, and the data signals required for effective, AI-enhanced monitoring and validation, while mentoring team members and influencing stakeholders on these aspects.