Senior ML Engineer

Relae (formerly Carbon Direct) helps organizations like Microsoft, Mitsubishi, and JPMorgan Chase make defensible, high-stakes decisions across energy strategy, decarbonization, environmental markets, and natural capital. Our teams combine scientific rigor with commercial and policy fluency to answer questions that matter, and we invest just as seriously in the people answering them, with a clear path for career growth, structured manager development, and real ownership of hard problems from day one.

The Role

Overview

Relae is seeking an AI/ML Engineer for our Power, Data and Innovation team. This role will be responsible for designing, building, training, and maintaining machine learning models for production. You will be directly working with Relae’s existing and prospective clients to provide insights and analysis on power hedging and procurement of electricity, gas and other energy commodities to help analyze operational and market risks and flexible load opportunities, including assessments of associated emissions impacts.

This is an individual contributor role, and this role will work closely with software engineers, data scientists, energy economists, and power systems engineers to build models that support hyperscaler, utility, and corporate energy buyer clients navigating the AI-driven data center power boom.

Responsibilities

• Leveraging a deep understanding of power systems data, identify, gather, and validate appropriate datasets for relevant power-sector applications, including assessment of uncertainties and data gaps, and develop analytical approaches to mitigate them.
• Design, implement, and maintain end-to-end ML pipelines for energy market forecasting and grid analytics (data ingestion from ISO/utility feeds and weather sources, feature engineering, model training, evaluation, deployment, and monitoring).
• Productionize forecasting and optimization models such as nodal price, load, or renewable generation forecasts with robust testing, observability, and documentation, in collaboration with cross-functional software and power advisory teams, and automated evaluations to ensure safe, frequent releases.
• Collaborate with power systems engineers and energy market analysts to translate research insights and domain expertise into performant, scalable forecasting and optimization systems.
• Contribute to technical design reviews, coding standards, and mentoring of best practices with the Tech & Software teams at Relea.

Required Qualifications

• 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, with hands-on experience in ML frameworks and time-series forecasting techniques applied to the power industry, along with a solid understanding of current ML approaches — including LLMs, predictive analytics, and agent-based modeling — as well as time-series-specific 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, including Relea’s existing and prospective clients
• Experience working in a fast-moving, ambiguous environment where the product surface may evolve based on client needs

Preferred Qualifications

• Power and energy advisory/consulting experience or experience in power product design
• Familiarity with weather-driven forecasting platforms or techniques (e.g., NWP data, ensemble weather models) and experience with grid optimization problems (e.g., storage dispatch, unit commitment, OPF)

Salary Range