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About the Role
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.
Responsibilities
- Design, prototype, and productionize optimization methods for large, non-linear, and mixed-integer problems.
- Formulate and solve problems for mitigation portfolio optimization, selecting least-cost combinations of upgrades, storage, and other measures.
- Build methods for economic analysis, including cost allocation, benefit-cost evaluation, and comparison of alternative portfolios.
- Develop MILP/MINLP formulations, AC/DC power flow relaxations, linearizations, decomposition approaches, and heuristics.
- Prototype methods in Python, validate against full AC power flow results, and deploy as scalable services.
- Verify optimized portfolios, quantify optimality gaps, runtime, and sensitivity to assumptions.
- Partner with transmission planners to capture real-world constraints and translate them into model constraints and objectives.
- Explain optimization results, trade-offs, and assumptions to engineers, clients, and stakeholders.
- Write methodology papers, model formulations, and validation reports.
Requirements
- Master's degree or PhD in Electrical Engineering, Operations Research, Applied Mathematics, or a related field, with a focus on power systems and/or optimization. A Bachelor's with equivalent experience will be considered.
- 3+ years of experience applying optimization to power systems problems in industry or research.
- Strong foundation in mathematical optimization: linear, mixed-integer, and non-linear programming, convex relaxations, and decomposition methods.
- Proficiency in Python and at least one optimization modeling framework (e.g., Pyomo, JuMP, CVXPY, PuLP, gurobipy) and commercial or open-source solvers (e.g., Gurobi, CPLEX, Xpress, HiGHS, Ipopt).
- Solid understanding of power flow, contingency analysis, and transmission planning concepts, including the US transmission system and ISO/RTO structures.
- Strong communication skills: able to explain complex models, assumptions, and results to engineers and non-technical audiences.
- Excellent problem-solving skills, especially in diagnosing infeasibility, numerical issues, and scaling challenges in large models.
- Hands-on transmission planning or interconnection study experience (steady-state, short circuit, dynamic stability) using tools such as PSS/E, PSLF, PowerWorld, TSAT, or TARA.
- Experience with transmission expansion planning, network upgrade cost allocation, or interconnection cluster studies.
- Experience with production cost or capacity expansion models and economic planning metrics.
- Experience with stochastic or robust optimization and scenario-based planning under uncertainty.
- Familiarity with power systems optimization packages (e.g., PowerModels.jl, pandapower, PyPSA, MATPOWER).
- Knowledge of machine learning methods used to accelerate optimization.
- Publications or open-source contributions in power systems optimization.
Skills
- Power Systems Optimization
- Mathematical Optimization
- Linear Programming
- Mixed-Integer Programming
- Non-linear Programming
- Convex Relaxations
- Decomposition Methods
- Python
- Optimization Modeling Frameworks (Pyomo, JuMP, CVXPY, PuLP, gurobipy)
- Solvers (Gurobi, CPLEX, Xpress, HiGHS, Ipopt)
- Power Flow Analysis
- Contingency Analysis
- Transmission Planning
- Communication Skills
- Problem-Solving
- Transmission Expansion Planning
- Network Upgrade Cost Allocation
- Production Cost Modeling
- Capacity Expansion Modeling
- Stochastic Optimization
- Robust Optimization
- Scenario-Based Planning
- Power Systems Optimization Packages (PowerModels.jl, pandapower, PyPSA, MATPOWER)
- Machine Learning for Optimization
- Software Engineering Practices (Git, testing, code review, deployment)
Location
- Remote
Work Type
- Remote
- Full-time
Experience Level
- 3+ years of experience
Education Level
- Master's degree or PhD in Electrical Engineering, Operations Research, Applied Mathematics, or a related field
- Bachelor's degree with equivalent experience
Benefits
- 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
- 401K, up to a 4% match (100% vested from day 1)
About the Company
- Electric Power Engineers (EPE) has partnered with power and energy clients across the globe for over half a century.
- Provides consulting expertise and energy intelligence software solutions for complex engineering and grid modeling challenges.
- Leaders in the renewables space, focused on building a modern, secure, and resilient grid.
Equal Opportunity
- EPE is an equal opportunity/AA/Disability/Veteran employer.