20 26 Ph DR es id en cy ,A ss et &D ef ec tS eg me nt at io n( Ta pe st ry )
Internship
Mountain View, CA (HQ)
About Tapestry
Tapestry is Alphabet's moonshot for the electric grid, working at the frontier where energy's complexity meets AI's potential. We were born at X, the innovation lab responsible for breakthrough technologies like Waymo, Verily and Google Brain.
To keep pace with humanity's growing energy needs, the world needs a grid that is visible and understandable. We provide that clarity by building advanced, AI-enabled analytical and planning tools that allow the entire energy ecosystem to plan smarter, move faster, and operate more efficiently-ensuring electricity remains reliable and affordable for everyone.
This is a global effort. Tapestry is proud to support partners in the U.S., U.K., Chile, New Zealand, Australia and Brazil as they build a cleaner, more resilient energy future. Joining Tapestry allows you to do the best work of your life as part of a multidisciplinary team of experts in AI, energy systems, software engineering and product design-all collaborating to reshape energy on a global scale. If you want to tackle problems that matter and build tools with real impact, we would love to meet you. Learn more about our team and our mission
here (https://www.tapestryenergy.com/en)
.
About the role
This PhD program focuses on advancing computer vision techniques for large-scale asset and defect segmentation using modern foundation and segmentation models. The goal of this work is to significantly reduce the time, data, and manual effort required to build accurate asset and defect detection systems, enabling faster iteration and stronger downstream model performance.
You will explore, proto@type, and evaluate segmentation-first approaches that improve how infrastructure assets are identified and understood from diverse image sources. This is a hands-on research role with a strong emphasis on translating cutting-edge methods into scalable, real-world applications.
How you will make 10X Impact
Accelerate model development: Enable faster creation of new asset and defect detection models by leveraging segmentation-first approaches that significantly reduce training time and data requirements.
Improve downstream model performance: Produce high-quality segmentation masks that serve as targeted attention mechanisms, improving accuracy and efficiency for detection and classification models.
Increase scalability: Help scale asset and defect detection across new asset @types and image sources without linear increases in manual labeling or compute costs.
Reduce operational friction: Decrease iteration cycles for model experimentation, allowing teams to validate and deploy improvements more quickly.
Drive real-world reliability: Contribute to AI-powered capabilities that improve infrastructure monitoring and preventive maintenance, directly supporting system reliability at scale.
What you should have:
PhD student specializing in Computer Vision, Machine Learning, or a closely related field
Strong experience with modern computer vision techniques, especially image segmentation
Proficiency in Python and deep learning frameworks commonly used for vision research
Experience designing, training, and evaluating vision models on real-world image data
Strong research instincts, including problem formulation, experimentation, and analysis
Passion for applying research to practical problems with real-world impact
It'd be great if you also had one or more of these:
Experience working with foundation or promptable segmentation models
Familiarity with building efficient data pipelines for vision tasks
Exposure to downstream model optimization or attention mechanisms
Experience applying vision models to large-scale or noisy real-world datasets
Interest in bridging research and production-oriented applications
Our values
Take charge : We take initiative and own outcomes that move the mission forward.
Transform with purpose: We build solutions that solve real problems and create meaningful impact.
Be a Tapestry, not a thread : We collaborate across diverse skills and perspectives to achieve more than we can individually.
Always fine-tune : We stay curious, seek feedback, and refine our understanding as we learn.
Stay grounded : We listen openly, value different perspectives, and stay focused on what matters most.
What we offer
Competitive salary
Medical, dental, and vision coverage
A culture that supports growth, ownership, and meaningful impact, along with:
Immersion in a world-class research environment at the intersection of AI and climate tech.
Competitive residency stipend and housing relocation support for the duration of the program.
Direct mentorship from industry-leading research scientists and engineers.
Opportunity to work on "moonshot" problems with access to Alphabet-scale compute and resources.
The US base salary range for this position is $109,000 - $150,000 + 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 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 benefits.
An Equal Opportunity Workplace
At X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
If you have a disability or special need that requires accommodation, please contact us at
x-accommodation-request@x.team
.
