Power Systems Research Scientist

Gridmatic Inc•Cupertino, CA

4d•$175,000 - $235,000

About The Position

We are looking for a Power Systems Research Scientist to develop physics-based models of large-scale transmission systems and their impact on electricity markets. You will work on large-scale optimization and simulation problems, including power flow, congestion, and security-constrained unit commitment and economic dispatch (SCUC/SCED). This role focuses on designing scalable algorithms and high-performance implementations for solving complex power system problems. This role sits at the core of our research and trading stack, building models and computational tools that directly impact how we understand and operate in electricity markets. We are particularly interested in rethinking power system optimization and simulation using modern computing (e.g., GPU acceleration).

Requirements

Advanced degree (MS/PhD) in Electrical Engineering, Power Systems, or related field
Strong background in power systems analysis and modeling
Experience with power flow (AC/DC), 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

Nice To Haves

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

Responsibilities

Develop and analyze power network models, including AC/DC power flow, contingency analysis, and security constraints
Build and enhance large-scale optimization models (e.g., 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 ML and trading teams to integrate network-aware signals into forecasting and decision systems