GridCARE · San Francisco
OtherEngineeringRemoteFullTime
Location: Redwood City, CA (Hybrid) or Remote – US
Team: Engineering
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.
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.
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 operate: their components, load behavior, operating constraints, and how that operation interacts with the electric grid.
Proficiency in Python or a similar programming language.
Ability to think from first principles and learn unfamiliar technical domains quickly.
Comfort working with ambiguity and open-ended questions.
Strong written and verbal communication skills.
Demonstrated ability to effectively use AI tools in technical workflows.
Exposure to infrastructure systems, energy systems, cloud computing, data centers, telecommunications networks, or other large-scale engineered systems.
Exposure to data center, GPU, CPU power consumption modeling.
Experience modeling data center to grid interaction, such as interconnection, load response, or capacity constraints.
Experience developing models used to support real-world operational or planning decisions.
Experience working with large datasets.
Work on a problem that directly affects the speed of deploying AI and energy infrastructure
Own technical direction on a product built from the ground up alongside domain experts
Meaningful organizational influence in a small, high-trust team
Competitive salary, equity, and benefits; hybrid in Redwood City with remote flexibility
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.
$140,000 – $250,000