Optimization Research Scientist

Gridmatic Inc. is a high-growth startup with offices in the Bay Area and Houston that is accelerating the clean energy transition by applying our expertise in data, machine learning, and energy to power markets. We are the rare startup that has multiple years of profitability without raising venture capital. Gridmatic is a great place to work with a culture that values teamwork, continuous learning, diversity, and inclusion. We move quickly and fix things. We are environmentally and data-driven, with a growth-oriented, academic mindset. We value integrity as much as excellence.

We are looking for an Optimization Research Scientist to expand the horizons of our technology as we work to accelerate the decarbonization of the electricity system. This role involves applied research, and hence the ideal candidate will possess a deep understanding of mathematical optimization methods, and will develop the knowledge and understanding necessary to apply them to energy markets. They will investigate new approaches to optimization problems we are currently solving, as well as designing optimization formulations for problems we don't yet solve. A successful candidate will embrace constant learning of both engineering and mathematical concepts, as well as economics and electricity market related topics.

What you might work on:

• Develop understanding of mathematics and mechanisms of energy markets in order to be able to discover and adapt appropriate methods to solve specific problems.
• Keep abreast of latest advances in optimization research in order to consider if any new methods are especially promising for problems in the electricity market space.
• Design and analyze optimization formulations for problems arising in battery operations, energy trading, and other business areas.
• Promote optimization based decision making and mathematical modeling across the company.
• Author research papers in the fields of optimization, operations research, and decision making under uncertainty.
• Write and review code, with attention to performance and readability.

You might be a good fit if you:

• have a strong publication record demonstrating expertise in mathematical optimization.
• have earned a PhD in applied mathematics, operations research, or a related quantitative field.
• have deep knowledge of optimization techniques, particularly for convex optimization problems. 
• show proven experience researching and applying optimization techniques.
• are able to write robust code in Python.
• are familiar with optimization libraries like CVXPY. 
• have outstanding analytical and problem-solving skills. 
• have excellent mentorship, communication, and teamwork skills. 
• have enthusiasm for learning. Knowledge of the energy industry a strong plus.




We recognize some candidates may hesitate to apply if they do not have all the listed skills. We encourage interested individuals to apply if they have relevant skills even if they do not have experience in every listed area.

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