PhD Residency, Machine Learning Explainability; Tapestry

Overview

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.

As a PhD Resident in Machine Learning Explainability, you will join Tapestry’s six-month PhD Residency Program to research how modern AI techniques—particularly large language models (LLMs) and graph-based models—can explain complex decision-making systems used in electric grid operations.

This role focuses on improving trust, transparency, and usability of highly constrained optimization engines such as economic dispatch, unit commitment, and long-term planning tools. Your work will explore how complex model outputs, constraints, and system behaviors can be translated into clear, human-understandable explanations for both expert and non-expert users.

• Research and prototype explainability approaches for complex optimization and decision-making systems in the electric grid.
• Apply LLMs to generate natural-language explanations of model outputs, constraints, and tradeoffs.
• Explore reasoning over graph-structured data (e.g., power grids) to produce grounded, faithful explanations.
• Investigate methods to detect and explain incorrect or anomalous network model inputs in natural language.
• Translate academic research into practical feasibility demonstrations using real or simulated grid data.
• Collaborate with machine learning researchers and power systems experts to refine approaches and evaluation methods.
• Clearly document findings and communicate insights to inform future research and product directions.

• Currently enrolled in a PhD program in Machine Learning, Computer Science, Electrical Engineering, or a related field.
• Strong research experience in machine learning or deep learning.
• Hands-on experience with LLMs, transformer-based models, or graph neural networks.
• Strong programming skills in Python and experience with frameworks such as PyTorch or JAX.
• Ability to reason about complex systems and communicate technical concepts clearly.
• Interest in applying AI to high-impact, real-world infrastructure challenges.

• Experience with optimization, convex optimization, or decision-making systems.
• Familiarity with power systems, energy modeling, or networked physical systems.
• Prior work in explainability, interpretability, or human-centered AI.
• Publications in ML or AI venues (e.g., NeurIPS, ICML, ICLR).
• Experience working on applied research projects in industry or startup environments.

• 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.

A culture that supports…