Software Engineer - Multiple Teams

• Modify infrastructure (training and serving) to support new modeling techniques and products.
• Prototype new infrastructure, automation, workflows, and tooling.
• Improve robustness, reliability, and usability of infrastructure.
• Analyze and debug performance, and make improvements.
• Collaborate closely with team members and partners to build systems that integrate seamlessly across the ecosystem.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages.
• 1 year of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
• 1 year 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.
• Experience with Machine Learning systems or Large Language Models (LLMs).
• Experience designing system architecture for enterprise computing environments supporting AI/ML workloads.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
• Experience in machine learning modeling or machine learning infrastructure.
• Experience designing distributed machine learning systems.
• Experience developing accessible technologies.
• Experience in Google's Machine Learning stack (e.g., Tensorflow, SAX, GeMax, TPUs).
• Experience in large-scale distributed training and model parallelism.

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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Pittsburgh, PA, USA.

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages.
• 1 year of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
• 1 year 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.
• Experience with Machine Learning systems or Large Language Models (LLMs).
• Experience designing system architecture for enterprise computing environments supporting AI/ML workloads.