This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior ML Engineer based in the United States.
This role offers an opportunity to build production-grade machine learning solutions addressing complex challenges in the energy sector. You will develop forecasting and optimization models that help organizations navigate electricity markets, energy procurement, power hedging, and grid operations. Working at the intersection of machine learning, power systems, and energy economics, you will turn scientific and market insights into scalable analytical products. You will collaborate with software engineers, data scientists, energy economists, and power systems specialists. Your work will support utilities, hyperscalers, and corporate energy buyers responding to growing electricity demand driven by AI and data centers. This individual contributor position offers meaningful technical ownership in a fast-moving, client-focused environment.
Accountabilities
You will design, develop, and maintain machine learning systems for energy market forecasting and grid analytics, ensuring that research-driven models become reliable, scalable production solutions.
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Identify, collect, assess, and validate power systems datasets for relevant industry applications, addressing data gaps, uncertainty, and quality challenges.
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Build and maintain end-to-end ML pipelines covering data ingestion from independent system operators, utilities, and weather sources, as well as feature engineering, model training, evaluation, deployment, and monitoring.
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Develop and productionize forecasting and optimization models, including nodal electricity prices, energy loads, and renewable generation forecasts.
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Implement robust testing, observability, documentation, and automated evaluations to support reliable and frequent model releases.
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Monitor developments in wholesale electricity and gas markets, congestion, basis, capacity, ancillary services, tariffs, and regulations that may affect portfolio costs or reliability.
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Work closely with power systems engineers and energy market analysts to translate domain expertise and research findings into scalable forecasting and optimization solutions.
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Contribute to technical design reviews, coding standards, and engineering best practices while supporting knowledge sharing across technical teams.
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Communicate model outputs, analytical findings, and technical trade-offs clearly to internal stakeholders and prospective or existing clients.
Requirements:
The ideal candidate combines advanced academic training with hands-on experience deploying machine learning models in the energy industry. You should bring strong Python and time-series forecasting expertise, sound technical judgment, and the ability to communicate complex analytical concepts to diverse audiences.
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Master’s degree or Ph.D. in Applied Statistics, Applied Economics, Computer Science, or a related field.
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Proven experience building and deploying production machine learning models in power trading, energy procurement, commodity risk management, utility strategy, energy markets, or a related domain.
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Strong Python programming skills and practical experience with machine learning frameworks and time-series forecasting techniques applied to the power industry.
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Solid understanding of modern machine learning approaches, including large language models, predictive analytics, and agent-based modeling.
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Knowledge of time-series challenges such as non-stationarity, seasonality, data leakage prevention, and appropriate cross-validation methods.
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Understanding of power markets and the ability to assess market data, operational constraints, and regulatory developments.
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Strong communication skills, with the ability to explain model results and technical decisions to non-technical stakeholders and clients.
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Ability to work independently in a fast-paced, ambiguous environment where product priorities and requirements may evolve.
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Experience in power and energy advisory or consulting, or in power product design, is preferred.
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Familiarity with weather-driven forecasting methods and platforms, including numerical weather prediction data and ensemble weather models, is an advantage.
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Experience with grid optimization challenges such as storage dispatch, unit commitment, and optimal power flow (OPF) is also preferred.
Benefits:
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Annual salary range of $159,000–$195,000 USD.
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Comprehensive nationwide medical, dental, and vision coverage.
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Flexible vacation policy and 10 company-wide holidays.
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Annual winter break between Christmas and New Year’s.
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16 weeks of fully paid parental and family leave, with no minimum tenure requirement.
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Remote-friendly working environment.
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Annual company-wide retreats.
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Reimbursement for home office setup.
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Benefits listed above apply to U.S.-based employees.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
