Modeling Engineer

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.

• 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.

Learn more about GridCARE:

• Tech Crunch:
  GridCARE thinks more than 100 GW of data-center capacity is hiding in the grid
• Utility Dive:
  Portland General Electric invests in AI-powered flexibility to speed data-center connection
• Data Center Dynamics:
  From Years to Months — Creating an AI Fast Lane for Data Centers
• GridCARE Raises $64 Million Series A to Create a New Category:
  Power Acceleration

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.

• Build models that accurately represent the physical systems inside of a data center
• Develop simulation frameworks that capture uncertainty, growth trajectories, and operational behavior across planning horizons.
• Explore how changes in technology, utilization, deployment strategies, and external factors influence future energy consumption.
• Analyze historical data to identify patterns, drivers, and sources of uncertainty.
• Evaluate model performance and continuously improve assumptions, methodologies, and predictive accuracy.
• Collaborate with engineers, researchers, and business stakeholders to translate practical questions into quantitative analyses.
• Communicate model assumptions, limitations, and findings clearly to both technical and non-technical audiences.
• Take ownership of key modeling components and drive them from concept through validation and deployment.

• Bachelor's, Master's, or PhD in a quantitative field, with 1–5 years of relevant industry or applied research experience.
• Experience working with data, mathematical models, simulations, forecasting problems, and time-series analysis.
• Experience with forecasting under uncertainty, probabilistic modeling, or scenario generation.
• A passion for understanding how data centers work and…