Develop and implement advanced optimization models for Security-Constrained Unit Commitment (SCUC), Security-Constrained Economic Dispatch (SCED), and other electricity market clearing processes.
Model electricity market behavior from the participant perspective (generation, storage, demand response), including bidding strategies, forecasting, and risk exposure.
Develop tools for network modeling, power flow analysis, and contingency analysis.
Work closely with engineers to productionize models, ensuring scalability and reliability.
Simulate market outcomes under various conditions, including congestion, renewable variability, storage operations, and policy changes.
Requirements
python
optimization
ai
hpc
ms/phd
iso/rto
Solid understanding of ISO/RTO market rules and participant behavior.
Experience with production cost modeling tools, HPC/cloud computing, or power system simulators.
Proficiency in Python and experience handling large datasets.
Strong background in optimization (mixed integer, stochastic, robust, convex) with applications to SCUC/SCED or other electricity market problems.
Familiarity with AI techniques in energy markets.
MS or PhD in Electrical Engineering, Power Systems, Operations Research, or a related field.
Benefits
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Training + Development
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Interview process
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Visa Sponsorship
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Security clearance
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Company
Overview
300 MW/1 GWh
Battery Storage Capacity
The company manages large-scale battery storage systems, optimized for energy trading and dispatch operations.
Founded 2016
Company Origin
The company was established in 2016 by Stanford-trained Matt Wytock to modernize energy markets.
Bootstrapped to profitability for multiple years before raising any venture funding.
Builds deep-learning models that forecast electricity prices and market dynamics in wholesale power markets.
Operates as a licensed Retail Electric Provider in ERCOT, offering energy supply and tailored contracts to industrial clients.