Software Engineer III, Quantum Computing Software, Quantum AI

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 2 years of experience programming in Python.
• Experience working with deep learning frameworks (e.g., TensorFlow, PyTorch, or JAX).
• Experience with Machine Learning.

Preferred qualifications:

• Master's or PhD degree in Computer Science, Machine Learning, Quantum Computing, Physics, or a related field.
• Experience with computer vision (e.g., image classification, object detection, segmentation).
• Experience with MLOps practices and deploying models into research or production environments.
• Experience with quantum computing concepts.
• Experience with Large Language Models (LLMs) and their application, or knowledge in exploring their potential for scientific discovery.

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 Software Engineer on the Applied Machine Learning team, you will play a pivotal role in advancing quantum computing through innovative AI solutions.

Your work will span two key strategic areas:

AI-Powered Research Acceleration: Explore, develop, and implement novel applications of Gemini to significantly enhance our team's research productivity, streamline complex quantum workflows, and unlock new scientific insights.

Computer Vision for Quantum Hardware: Design, train, and deploy sophisticated computer vision and other machine learning models to optimize critical aspects of our quantum hardware. This includes improving device fabrication processes, enabling automated calibration, and performing robust quality control analysis.

You will contribute to projects within these focus areas, typically with 6-8 month iterative cycles, transforming research ideas into tangible applications that directly impact our quantum capabilities.

The full potential of quantum computing will be unlocked with a large-scale computer capable of complex, error-corrected computations. Google Quantum AI's mission is to build this computer and unlock solutions to classically intractable problems. Our roadmap is focused on advancing the capabilities of quantum computing and enabling meaningful applications.

The US base salary range for this full-time position is $141,000-$202,000 + 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

• Develop and deploy innovative AI solutions, including leveraging Gemini, to significantly accelerate quantum research, streamline complex workflows, and unlock new scientific insights.
• Design, train, and integrate advanced computer vision and other Machine Learning (ML) models to optimize quantum hardware fabrication, automate device calibration, and enhance quality control analysis.
• Drive the full life-cycle of ML projects from feasibility studies and data preparation through iterative model development, rigorous bench-marking, and collaborative implementation to transform research ideas into tangible quantum advancements.
• Participate in design for new features for ML infrastructure and projects.