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Staff Machine Learning Engineer, Personal AI, Pixel at Google
Mountain View, United States


Job Descrption

Minimum qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience in software development, and with data structures/algorithms.
  • 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
  • 2 years of experience in applied machine learning/machine learning research, and with Python or C++ coding.

Preferred qualifications:

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  • 3 years of experience working in an organization involving cross-functional, or cross-business projects.
  • Experience with Android development, and with natural language processing (NLP) or Computer Vision.
  • Experience with large language models (LLM).

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 team builds Pixel AI features based on large language models. In this role, you will manage data collection design, data engineering, feature engineering, model training, and model evaluation. You will work with engineers, researchers, and product managers across Google to design and implement new features for pixel devices.

Google's mission is to organize the world's information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, Software, and Hardware to create radically helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices & Services team is making people's lives better through technology.

The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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 maintain machine learning models, and collect and clean data to tune and evaluate machine learning models.
  • Design evaluation framework and carry out evaluations of machine learning models.
  • Stay up-to-date on the latest machine learning techniques and technologies and apply them to technical solutions.
  • Develop applications on Android/Pixel.
  • Propose, design, and implement data collection solutions, and decompose use cases into tractable technical problems.
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