Gridmatic

Machine Learning Engineer

• Assist in designing and implementing machine learning models for electricity grid forecasting.
• Explore and prototype ML algorithms for generative time-series forecasting.
• Support the extension and improvement of existing ML libraries and frameworks.
• Run experiments and analyze results to improve model performance.
• Help monitor and evaluate the performance of production models.
• Contribute to team discussions, brainstorming, and problem-solving.
• We are seeking a motivated Machine Learning Intern to help design and test forecasting models that accelerate the decarbonization of the electricity grid. This role is ideal for students or recent graduates who want to apply their programming and analytical skills in a fast-paced environment, learn from experienced ML engineers, and contribute to solving real-world challenges in energy and climate. You will have the opportunity to work on cutting-edge problems in generative time-series forecasting, collaborate with a team of talented engineers and researchers, and see your ideas tested in real-world applications.

Machine Learning Intern

Systems Engineer

• Gridmatic Inc. is a high-growth startup accelerating the clean energy transition by applying data, machine learning, and energy expertise to power markets.
• Simulate and develop models and tools to analyze the electricity energy market, including simulating ancillaries and transmission constraints.
• Contribute to development of power market analysis tools in network modeling, network analysis, power flow, security/contingency analysis, and real-time control.
• Apply familiarity with Security Constrained Unit Commitment (SCUC) and Security Constrained Economic Dispatch (SCED) and power system dynamics and stability.
• Perform advanced quantitative and economic analysis of energy markets to assess power plant operation and valuation, emissions, transmission, and regulatory effects.
• Stay abreast of market developments and provide fundamental analysis, insights, and perspectives on electricity markets.
• Utilize optimization techniques such as convex programming, mixed integer programming (MIP), linear programming (LP), and stochastic optimization where applicable.

Power System Engineer Intern

Data 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.
• As a Data Science Intern, you'll support development of software products for our clean electricity retail, battery storage, and market simulation teams.
• You'll analyze electricity market datasets and contribute to cross-functional software products spanning disciplines of machine learning, optimization, and backend software engineering.
• Build a storage scheduler in a new market: Collect new signals necessary for modeling
• Read and implement constraints defined in electricity market tariffs
• Work with the ML Team to create the necessary forecasts
• Work with the Optimization Team to solve the portfolio optimization
• Create a pricing engine for time-matched (24/7) clean electricity supply: Ingest data on clean energy supply contracts, load shapes, and electricity prices
• Work with the ML Team to forecast retail customer load
• Work with the Optimization Team to create a least-cost supply portfolio

Data Science Intern

Application Engineer

• Gridmatic Inc.: high-growth clean-energy data and ML startup accelerating the clean energy transition via power market analytics
• Simulate and develop models and tools to analyze the electricity energy market, including simulating ancillaries and transmission constraints
• Contribute to development of power market analysis tools in network modeling, network analysis, power flow, security/contingency analysis, and real-time control
• Familiarity with Security Constrained Unit Commitment (SCUC) and Security Constrained Economic Dispatch (SCED) combined with background in power system dynamics and stability
• Perform advanced quantitative and economic analysis of energy markets to assess future electric power market and prices, power plant operation and valuation, environmental emissions and compliance, transmission, and regulatory effects
• Stay abreast of market developments and provide fundamental analysis, insights, and perspectives on electricity markets
• Familiarity with optimization techniques such as convex programming, mixed integer programming (MIP), linear programming (LP), and stochastic optimization is a plus

Power System Applications Engineer

AI Research Scientist

The Company
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.

The Role
We are looking for an AI 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 forecasting techniques, and will develop the knowledge and understanding necessary to apply them to energy markets. They will investigate new technologies to better solve problems we already model, as well as designing solutions to problems we don’t yet solve. In addition, they will work to generalize these solutions so they can be applied more broadly, including on academic datasets for the purpose of sharing with the academic community via publication. A successful candidate will embrace constant learning of both engineering and mathematical concepts, as well as economics and electricity market related topics.

Research Scientist

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.

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.
Develop predictive models that generate optimization inputs, such as predicting future energy prices.
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.

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 looking for a Machine Learning (ML) Engineer to develop deep learning forecasting models to help quickly decarbonize the electricity system.
This is the ideal role for someone who wants to use their programming skills in a fast-paced environment, enjoys learning every day, and enjoys bouncing ideas off coworkers and collaborating on tough problems.

Full-stack Engineer

The Company: 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’re looking for backend and product engineers to work on a wide range of engineering problems which relate to energy, automation, and ML forecasting.
- Product engineering (building products and automation to make it easier to trade and sell energy, optimize batteries, and more)
- Data pipelines (reliable data ingestion for ML models)
- ML infrastructure (orchestration of ML jobs, data loading, GPUs)
- Backend infrastructure (setting new Kubernetes services, observability)
- Developer velocity through tooling and automation (CI/CD, Buildkite, dependency management)
Our stack includes: Python, GCP (Google Cloud), Kubernetes, Terraform, Flyte, React/NextJS, Postgres, and BigQuery.
We think it’s important to be able to work in the office in order to do design/whiteboarding, and have more opportunities for face-to-face time and mentorship. 3 days in-office in Cupertino is mandatory, but 4-5 days is preferred.