Lead Engineer - Advanced Computational Methods Power Systems

Lead Engineer – Advanced Computational Methods for Power Systems

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Eaton’s CTO Corporate Technology Office division is currently seeking a Lead Engineer – Advanced Computational Methods for Power Systems. This position is based at the Eaton Research Lab in Golden, CO.

The expected annual salary range for this role is $99,749.97 – $ a year. Please note that salary information shown above is a general guideline only. Salaries are based upon candidate skills, experience, and qualifications, as well as market and business considerations.

In this role, you will help shape the future of energy systems by developing advanced computational methods and exploring emerging technologies like quantum-inspired algorithms and machine learning to improve grid resilience and efficiency. Working with cloud-based platforms and collaborating with partners using cutting-edge quantum hardware, you’ll lead projects that bridge innovation and practical impact for sustainable power solutions.

The ERC is a corporate R&D function within Eaton's Chief Technology Office, responsible for innovative and breakthrough technology development and commercialization for Eaton products and service solutions. This position will lead the R&D of advanced computing techniques to enhance energy resilience for the electric grid and connected energy systems. The Lead Engineer will report to the Technology Manager and help identify key technical gaps and influence technology roadmap for Eaton’s products and services.

This work will help businesses across Eaton solve critical business problems and uncover new business opportunities.

• Serve as a technical contributor and/or lead in technology development and demonstration.
• Serve as an individual contributor and subject matter expert in government and non-government proposal development and/or project execution related to all aspects of advanced computation environment.
• Lead development of advanced computational methods to improve energy resilience and support the transition to sustainable power systems.
• Design and integrate quantum-inspired algorithms for energy systems applications like contingency analysis, leveraging cloud-based workflows and partner quantum platforms.
• Build and deploy machine learning solutions to enhance reliability and reduce errors in emerging quantum technologies.
• Collaborate with internal teams and external partners to validate solutions across diverse hardware and cloud environments.
• Define and track performance metrics to ensure solutions meet technical and operational goals.
• Investigate, integrate, and validate new product designs abiding by time-based milestones. Engage multiple business stakeholders, cross‑functional teams, National labs, Universities, and Customers.
• Innovate, invent, and challenge state‑of‑art design & process by securing critical intellectual properties through patents and publications in reputable peer‑reviewed journals.

Required (Basic)

Qualifications:

• PhD degree or PhD candidate within 6 months to graduation in Electrical Engineering, Computer Science, or related field from an accredited institution, OR
• Master’s degree in Electrical Engineering, Computer Science, or related field from an accredited institution plus 3 years of industrial experience, OR
• Bachelor’s degree in Electrical Engineering, Computer Science, or related field from an accredited institution plus 5 years of industrial experience.
• Candidates should have experience in advanced computational methods for power systems including quantum-inspired algorithms and/or high-performance computing (HPC).
• Proficiency in Python and MATLAB for algorithm development is essential, along with familiarity with machine learning frameworks such as PyTorch or Tensor Flow.
• Eaton will not consider applicants for employment immigration sponsorship or support for this position.
• No relocation is offered for this position. All candidates must currently reside within 50 miles of Golden, CO to be considered. Active‑Duty Military Service member candidates are exempt from the geographical area limitation.

Preferred…