Senior ML Engineer

Relae

. Design, build, train, and maintain production machine learning models .

Posted 11/6/2026full-timeRemote • United StatesSenior💰 $159,000 - $195,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying machine learning models specifically for the power sector, with a strong focus on energy market forecasting and optimization. Proficient in Python and familiar with ML frameworks, time-series forecasting techniques, and translating complex technical insights for diverse audiences.

Highest-signal resume keywords

Machine Learning Model DeploymentPython ProgrammingTime-Series Forecasting TechniquesEnergy Market AnalyticsApplied Statistics or Economics Degree

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningFeature EngineeringModel TrainingModel EvaluationEnd-to-End ML PipelinesPredictive AnalyticsAgent-Based ModelingData ValidationAutomated EvaluationsCross-Validation Techniques

Soft Skills

Clear CommunicationCollaborationMentoring

Tools & Technologies

ML FrameworksData Ingestion ToolsObservability Tools

Industry Keywords

Power TradingEnergy ProcurementCommodity Risk ManagementWholesale Power MarketsRegulatory Changes

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Design, build, train, and maintain production machine learning models
Identify, gather, and validate power-sector datasets, including uncertainties and data gaps
Develop analytical approaches to mitigate data gaps
Design, implement, and maintain end-to-end ML pipelines for energy market forecasting and grid analytics
Ingest data from ISO, utility, and weather sources
Perform feature engineering, model training, evaluation, deployment, and monitoring
Productionize nodal price, load, and renewable generation forecasting and optimization models
Implement robust testing, observability, documentation, and automated evaluations
Collaborate with software engineers, data scientists, energy economists, power systems engineers, and energy market analysts
Translate research insights and domain expertise into scalable forecasting and optimization systems
Contribute to technical design reviews, coding standards, and mentoring of best practices

Requirements

What you’ll need

Experience building and deploying machine learning models in a production environment in power trading, energy procurement, commodity risk management, utility strategy, energy markets, or related roles
A master’s or a Ph.D. degree in applied statistics, applied economics, computer science, or a related field
Monitor real-time data for wholesale power markets, gas markets, congestion, basis, capacity, ancillary services, tariff developments, and regulatory changes that could affect portfolio cost or reliability
Strong Python skills
Hands-on experience with ML frameworks and time-series forecasting techniques applied to the power industry
Solid understanding of current ML approaches, including LLMs, predictive analytics, and agent-based modeling
Understanding of time-series challenges such as non-stationarity, seasonality, avoiding data leakage, and appropriate cross-validation techniques
Ability to translate model outputs and technical tradeoffs into clear communication for non-technical audiences and clients
Experience working in a fast-moving, ambiguous environment where the product surface may evolve based on client needs

Benefits

Comp & perks

Comprehensive nationwide medical, dental, and vision coverage
Flexible vacation policy and ten company-wide holidays
Annual winter break between Christmas and New Year's
16 weeks of fully paid parental and family leave with no tenure requirement
Remote-friendly work culture with annual company-wide retreats
Reimbursement for work-from-home setup