Computational Scientist, Grid Optimization; Tapestry

About Tapestry

Tapestry is an energy intelligence company incubated at X, the Moonshot Factory, the innovation lab within Alphabet. We are building the intelligence layer for the future electric grid. Our mission is to make the grid visible, predictable, and optimized through advanced AI systems so clean and reliable energy is available for everyone.

The grid is one of the most important and complex systems in the world. It is under increasing pressure from electrification, renewable adoption, and climate‑driven volatility. Tapestry creates the software and models that help utilities and grid operators understand what is happening, anticipate what is coming, and respond with confidence.

Our team brings together experts in AI, energy systems, software engineering, and product design. We operate with the agility of a young company supported by the technical rigor and resources in Alphabet’s ecosystem.

If you want to work on problems that matter and build tools with real impact, we would love to meet you.

As a Computational Scientist in Grid Optimization, you will be the architect of the decision‑making logic that powers Tapestry. This is a high‑seniority role designed for an expert who can bridge the gap between deep power systems theory and high‑performance software execution. You won't just be using existing solvers; you will be evolving our internal optimization engines to handle the unprecedented complexity of modern energy resources.

You will lead the design of scalable solvers for critical grid functions like ACOPF, SCED, and SCUC, ensuring they are fast enough and robust enough for real‑world deployment. If you are passionate about translating complex non‑convex optimization problems into production‑ready code that changes how the world uses energy, we want to talk to you.

• Drive the evolution of internal optimization engines to support the transition to high‑renewable energy grids.
• Act as a technical bridge between power system theoretical research and high‑performance software execution.
• Identify and resolve computational bottlenecks in large‑scale grid simulations to enable real‑time decision‑making.
• Lead the architectural design of scalable solvers that can handle the complexity of modern, decentralized energy resources.
• Work with power system and software engineering experts on the design and creation of advanced grid optimization tools for power grid such as ACOPF, SCED, and SCUC.

• Master or PhD in a STEM field (such as Electrical Engineering, Computer Science, Physics, Mathematics, or a related field).
• A strong interest in grid computations, particularly regarding optimization algorithms.
• Demonstrated proficiency in power systems core concepts.
• Significant background in the development of power systems optimization software tools.
• At least a minimum of 6 years professional experience focused on power systems modeling and optimization principles.
• Experience in one or more general‑purpose programming languages such as Python, Java, or C/C++.
• Deep understanding of numerical methods and convex/non‑convex optimization solver technologies.
• Proven ability to translate complex mathematical formulations into maintainable, production‑ready code.
• Experience performing sensitivity analysis and security assessments on large‑scale power flow models.
• Strong collaborative skills to work across multi‑disciplinary teams of AI researchers and hardware engineers.

• Familiar with electricity power markets and grid operations.
• A skilled and efficient programmer in scientific programming languages (e.g., JAX, C++, Julia, etc.) with a desire to expand into new areas of coding languages and processes.
• Familiar with Machine Learning concepts.

• Take charge
  :
  We take initiative and own outcomes that move the mission forward.
• Purpose in everything
  :
  We build solutions that solve real problems and create meaningful impact.
• Stronger together
  :
  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…