Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

Check how this applies to your own job

Skills this job asks for

Say which you have and which you want to build. We use it to match you to jobs like this one.

About the job

Overview

We are designing the grid of the future!

We are seeking an experienced R&D Engineer III to join our EPE Research and Development team. This role combines expertise in power systems engineering with advanced computational research — including machine learning, adaptive control, and simulation-based modeling — to develop next-generation models that support grid planning, control, and reliability. The primary responsibility is to design, develop, and validate research-driven solutions and modeling frameworks that address emerging challenges in modern power systems, including data center interconnection and large-scale system stability. You will work closely with power system engineers, software developers, and research scientists to translate cutting-edge research into practical, deployable models and tools.

Responsibilities

How you can make an impact:

Research & Model Development: Design, develop, and validate advanced modeling techniques — including machine learning-based surrogate models, digital twins, and stability certification methods — to support power systems planning and control applications.

Automation & Programming: Work with Python and other relevant languages to automate research workflows, build adaptive control tools, advanced generator/load models, and streamline power system simulations and analysis.

Technical Demonstration & Dissemination: Communicate research findings and technical model capabilities through internal presentations, technical reports, and peer-reviewed publications; support technical demonstrations for internal teams and clients as needed.

Collaboration: Partner with power system engineers, software developers, and cross-functional research teams to integrate novel modeling approaches into EPE's engineering and software offerings.

Client & Stakeholder Support: Serve as a technical resource for translating research capabilities into tailored solutions for client engagements and internal product development.

Validation & Troubleshooting: Test, validate, and troubleshoot research software and models to ensure accuracy, reliability, and performance in real-world power system contexts.

Qualifications

Bring your passion, here's what’s needed:

Ph.D. or Master's degree in Electrical Engineering with a focus on power systems, controls, or applied machine learning; a Ph.D. is strongly preferred given the research-intensive nature of this role.

Demonstrated research experience (through publications, thesis work, or applied projects) in power systems stability analysis, control systems, or machine learning applications in energy systems.

Deep understanding of power systems fundamentals, including transmission and distribution planning, grid dynamics, and stability analysis.

Strong programming skills in Python, with experience using deep learning frameworks (e.g., PyTorch, TensorFlow) and scientific computing libraries.

Strong presentation and technical writing skills — capable of explaining complex research concepts to both technical and non-technical audiences.

Familiarity with software development and testing practices to ensure robust, reliable research tools.

Excellent problem-solving skills, particularly in diagnosing and resolving issues in simulation-based or data-driven power system models.

Preferred Qualifications:

Proficiency in power system simulation software such as PSS/E, PSCAD, PSLF, Aspen, or TARA.

Experience conducting power system studies such as Steady State, Short Circuit, or Dynamic and Transient Stability analysis.

Experience with model predictive control (MPC), meta-learning, or in-context learning methods applied to dynamical or physical systems.

Experience developing digital twins or surrogate models for grid-connected assets (e.g., data centers, BESS, renewable generation).

Familiarity with cloud-based software development and integration of power system models.

Knowledge of AI-driven or large language model (LLM)-based applications in power systems.

Track record of peer-reviewed publications and/or peer review service for IEEE or related technical venues.

Experience mentoring junior researchers, interns, or students.

Be a part of an innovative team shaping the grid of the future through advanced energy intelligence. For more than half a century, Electric Power Engineers (EPE) has partnered with power and energy clients across the globe, providing consulting expertise and energy intelligence software solutions for complex engineering and grid modeling challenges. As leaders in the renewables space, we are focused on building a modern, secure, and resilient grid. Join us in making an impact on the communities we serve and the environment in which we live. Together we can transform the future of energy.

How we support you:

Comprehensive health and wellness benefits including medical, dental, and vision with 100% premium coverage for you

Generous PTO and paid holidays

MyShare Employee Ownership Program

Work with industry leaders

401K, up to a 4% match (100% vested from day 1)

Location : This position will be located in City, State

Travel : Occasional travel may be needed (10% or less)

EPE is an equal opportunity/AA/Disability/Veteran employer. The EEO is the Law poster, and its supplement are available using the following links: EEOC is the Law Poster

Third-Party Recruiting Notification

EPE does not accept unsolicited resumes from third-party recruiters . Any unsolicited third-party resumes forwarded by recruiters to EPE via our career page or to any of our managers or employees will be considered public information, may be treated as a direct application from the person identified in the resume, and will not be eligible for placement fee payment to the agency. EPE will not pay a fee to a third-party recruiter or agency without a previously signed third-party agreement and has not coordinated their recruiting activity with the appropriate member of the Talent Acquisition team.