Founding Reservoir Engineer / Reservoir Geologist (Modeling & Simulation)

UniversalAGI

2d•Onsite

About The Position

As a founding Reservoir Engineer / Geologist, you'll be in the arena from day one, building the future of AI for physics simulation with your own hands. This is your chance to take everything you've learned in reservoir geology and use it to build foundation AI models that will transform how the entire industry works. You'll work directly with the CEO and founding team to shape our product around the real pain points you've faced in your career: the tedious data preparation, the manual grid and upscaling loops, and endless history-matching. This role is for someone ready to push beyond the limits of traditional reservoir simulation, tackle deep uncertainty and scale challenges, and help define the next generation of subsurface engineering and decision-making.

Requirements

M.S. or Ph.D. in Reservoir Engineering, Petroleum Engineering, Geology/Geoscience, or closely related field (required).
3+ years of hands-on industry experience doing reservoir modeling and reservoir simulation end-to-end, ideally in an operator, service company, or subsurface consultancy setting.
Expert in reservoir simulator frameworks (e.g., SLB ECLIPSE / INTERSECT, CMG (IMEX/GEM/STARS), tNavigator, Nexus, or similar) and comfortable working directly with simulation decks / control files.
Deep understanding of subsurface flow and numerical simulation: multiphase Darcy flow, PVT, relative permeability & capillary pressure, well modeling/controls, initialization, boundary conditions, convergence/stability, and common failure modes.
Experience with model building and conditioning: gridding, upscaling, faults/barriers, contacts, rock/fluid property population, and rigorous QC to ensure simulation-ready inputs.
Ability to design and execute systematic scenario studies (sensitivities, uncertainty ensembles, development plan variants) and produce clean, explainable outputs that can serve as ground truth for ML.
Ability to design and execute systematic scenario studies (sensitivities, uncertainty ensembles, development plan variants) and produce clean, explainable outputs that can serve as ground truth for ML.
Outstanding execution velocity and delivery, with strong attention to detail and a data-quality mindset.
Exceptional creativity, problem-solving ability, ambition, and attention to detail.
Comfortable working in high-intensity, fast-paced startup environments with evolving priorities and tight deadlines.

Nice To Haves

Prior experience integrating AI/ML (or proxy/surrogate models) into subsurface workflows, or partnering with ML teams to define training targets and validation.
Experience at high-growth AI / deep-tech startups (Seed to Series C) or in a forward-deployed / client-facing technical role.

Responsibilities

Design, preprocess, grid, simulate, and post-process reservoir models (geological and flow) at unprecedented scale and speed, using industry-standard simulators and real subsurface data.
Develop systematic workflows to generate hundreds or thousands of reservoir scenarios, including geological realizations, well configurations, and operating conditions—building the data pipelines that will train our foundation models.
Guide the development of our product and our AI models for physical systems by working directly on research problems with our AI team, bridging the gap between traditional reservoir simulation and modern machine learning.
Work directly with clients to define benchmark datasets, validation criteria, and end-to-end workflows that demonstrate our models can match or exceed conventional reservoir simulation accuracy and decision value.
Generate high-quality simulation data at scale, every simulation you run directly improves our foundation models.
Advise the AI research team on the nuances that matter in subsurface modeling: model fidelity, black-oil vs compositional vs thermal models, gridding and upscaling tradeoffs, relative permeability, well controls, and numerical stability.
Lead technical conversations with customers and internal teams, translating between the language of reservoir engineering / geology and the world of AI and foundation models.