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Gridmatic

AI-driven optimization of renewable assets

Power Systems Research Scientist

Power Systems EngineeringConfirmed live in the last 24 hours
Full-timeCupertino, CA, USA$175k - $270kIn-person

About the Job

Requirements

  • An advanced degree (MS or PhD) in Electrical Engineering, Power Systems, or a related field.
  • A strong background in power systems analysis and modeling.
  • Experience with AC/DC power flow, transmission modeling, and congestion analysis.
  • Familiarity with ISO/RTO markets and network-constrained market outcomes.
  • Experience with optimization algorithms and large-scale mathematical programming.
  • Understanding of numerical methods for convex and/or non-convex optimization.
  • Strong programming skills in Python.

Responsibilities

  • Develop and analyze power network models, including AC/DC power flow, contingency analysis, and security constraints.
  • Build and enhance large-scale optimization models, such as SCUC/SCED, with detailed transmission constraints.
  • Design and implement scalable algorithms and solver components for large-scale power system optimization.
  • Identify and address computational bottlenecks in network-constrained simulations and optimization.
  • Model and analyze congestion and transmission-driven market outcomes.
  • Simulate grid scenarios with high penetration of renewables, storage, and outages.
  • Collaborate with machine learning and trading teams to integrate network-aware signals into forecasting and decision systems.

Desired Qualifications

  • Experience with tools such as PSS/E, PowerWorld, PSLF, or similar.
  • Familiarity with SCUC/SCED implementations.
  • Background in electricity market modeling or trading.
  • Experience working with large-scale datasets and cloud applications.
  • Familiarity with key power systems concepts such as PTDFs (power transfer distribution factors) and security constraints.
  • Experience with GPU-accelerated computing for large-scale optimization or simulation.
  • Experience with frameworks such as PyTorch or JAX for high-performance numerical computing.

About the Company

Gridmatic

AI-driven optimization of renewable assets

Gridmatic uses artificial intelligence to improve the profitability and reliability of renewable energy assets. It serves three client groups: generators of renewables, operators of energy storage, and consumers of renewable energy. The product suite analyzes market data with machine learning to forecast energy prices, manage price and performance risk, and optimize revenue from energy transactions—especially for storage assets that must balance risk and availability. Unlike others that focus on a single segment, Gridmatic combines AI analytics with energy market expertise to optimize both generation and procurement across markets. Its goal is to accelerate clean energy adoption by making renewable energy cheaper, more predictable, and easier to deploy at scale, supporting decarbonization and a more reliable grid.

Company size
51-200
Company stage
Early VC
Total funding
$6M
Headquarters
Cupertino, CA
Founded
2020

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