About Us

GridCARE is a leading venture-backed startup solving the most critical constraint in AI’s growth trajectory: immediate access to power. As demand for computing skyrockets, access to energy has become the defining bottleneck in the AI infrastructure race. While leading tech companies invest billions in speculative, long-term solutions that may take decades to arrive, GridCARE’s pioneering physics-based generative AI platform unlocks gigawatts of hidden capacity in today’s electric grid — enabling hyperscalers, data center developers, and utilities to power AI infrastructure years sooner than conventional approaches and without costly upgrades.

Founded at Stanford’s Doerr School of Sustainability and backed by leading investors, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure.

At GridCARE, you will:

• Work at the intersection of AI, energy, and infrastructure — the foundation of the next industrial revolution.

• Partner with hyperscalers, developers, and utilities on high-impact, real-world deployments.

• Help shape a more abundant, efficient, and resilient energy future for the digital era.

• Join a company defining a new category — capacity acceleration for AI.

• Receive competitive compensation, equity, and benefits in a fast-growth, mission-driven environment.

About the role

We're looking for an engineer in the early stages of their career who has demonstrated the ability to solve difficult technical problems in a professional or research setting to work on hard computational problems in the electric grid. This role is ideal for someone who enjoys solving difficult optimization problems and building software that has a direct impact on critical infrastructure. You’ll work closely with experienced engineers and researchers to build tools that help make the grid more flexible, efficient, and resilient as data centers, renewables, storage, and new regulations reshape how electricity systems operate.

What you’ll do

• Build optimization and simulation software for large-scale electric grid planning and operations problems.

• Develop models and algorithms for resource scheduling, network constraints, system reliability, and large-scale operational planning.

• Translate mathematical ideas into clear, tested, maintainable code.

• Analyze computational performance, identify bottlenecks, and improve reliability.

• Learn, customize, and fine-tune optimization algorithms.

• Work with real-world data and constraints from large physical systems.