Get more replies from employers
Send a job-specific resume in minutes.
GridCARE is seeking a quantitative engineer to build models that describe data center power usage and its evolution. You will develop simulation and forecasting models for current and future data centers to inform planning from intraday operations to multi-year infrastructure.
Ideal candidates have 1–5 years in data-driven modeling, experience with time-series forecasting, and strong Python skills. You will collaborate with AI and energy-domain experts in a fast-growing startup.
Fastminds is a recruiting partner of Gridcare.
We are helping Gridcare find a Physical Systems Modeling Engineer.
Location: Redwood City, CA — Hybrid (3 days/week in office)
Type: Full-time
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 climate-tech and deep-tech investors, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure.
Learn more about GridCARE:
We are looking for a quantitative engineer to help build the models at the core of how GridCARE understands data center power: how facilities consume energy today, and how that consumption will evolve over time. In this role, you will develop simulation and forecasting models for existing and future data centers and the components inside data centers that will be used to support planning decisions across the full horizon, from intraday operations to multi-year infrastructure development. The goal is not simply to predict future demand, but to build models that can generate realistic future operating scenarios under a wide range of assumptions and conditions.
This role is well suited for someone who enjoys reasoning about complex systems, working with imperfect data, and translating real-world behavior into quantitative models. You should be excited to learn new domains and apply rigorous analytical thinking to difficult problems.
We believe small teams of strong engineers can solve important, real-world infrastructure problems. We value clear thinking, strong ownership, pragmatic execution, and systems that hold up in production. If you're excited about technical leadership at the intersection of energy, infrastructure, and modern SaaS — we'd love to talk.