Computational Scientist, Grid Optimization

Overview

Staff Computational Scientist, Grid Optimization, Tapestry — Mountain View, CA

About Tapestry

Tapestry is a team within Alphabet building the AI-powered electric grid. We aim to make the energy system more visible, understandable, reliable, affordable, abundant, and clean. Originally born at X, Alphabet’s moonshot factory, Tapestry combines energy, AI, software, engineering, and product expertise to help the electricity ecosystem plan smarter, move faster, and operate more efficiently. This is a global effort with partners across the U.S., U.K., Chile, New Zealand, Australia, and Brazil.

Joining Tapestry means doing high-impact work with a multidisciplinary team tackling problems at global scale.

About the role

As a Computational Scientist in Grid Optimization, you will architect the decision-making logic that powers Tapestry. This high-seniority role bridges deep mathematical theory and high-performance software execution. You will evolve our internal optimization engines to handle the complexity of modern energy resources, not just using existing solvers but developing scalable production-ready solvers.

You will lead the design of scalable solvers for grid functions like ACOPF, SCED, and SCUC to enable real-world deployment. If you are passionate about translating complex non-convex optimization problems into production-ready code that changes how energy is used, 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 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 such as ACOPF, SCED, and SCUC.

• Master or PhD in a STEM field (e.g., Operations Research, Electrical Engineering, Computer Science, Physics, Mathematics, or related).
• Strong interest in grid computations and optimization algorithms.
• Demonstrated proficiency in power systems concepts.
• Significant background in developing optimization software tools.
• At least 6 years of professional experience in modeling and optimization.
• Experience in Python, Java, or C/C++.
• Deep understanding of numerical methods and convex/non-convex optimization solvers.
• Ability to translate complex mathematical formulations into maintainable, production-ready code.
• Strong collaborative skills to work across multi-disciplinary teams of AI researchers and hardware engineers.

• Experience with cloud platforms such as AWS, GCP, or Azure.
• Experience performing sensitivity analyses and security assessments on large-scale power flow models.
• Familiarity with electricity power markets and grid operations.
• Proficiency in scientific programming languages (e.g., JAX, Julia, C++, etc.).
• Familiarity 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 listen openly, value different perspectives, and stay focused on what matters most.

A culture that supports growth, ownership, and meaningful impact, along with:

• Competitive salary and equity
• Medical, dental, and vision coverage
• Generous PTO and flexible hybrid work model
• 401(k) with employer contribution
• The ability to work on important real-world problems within an Alphabet-backed environment

The US base salary range for this full-time position is $207,000 - $300,000 + bonus + equity + benefits. 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.