Geophysics + Seismic Researcher

📍 San Francisco | 🏢 5 Days Onsite

Location: Onsite in San Francisco

Compensation: Competitive Salary + Equity

Engineering simulation is one of the last major categories of software that AI hasn't rebuilt. The tools used to design aircraft, ships, reservoirs, and medical devices still run on numerical methods that are decades old, and an engineer can wait a full day for a single answer. UniversalAGI is building foundation models that learn physics directly from data, and they are already running in early deployments on real computational fluid dynamics and reservoir engineering problems for some of the largest industrial and defense organizations in the world.

We are a team of 25 researchers and engineers in San Francisco backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), Prith Banerjee (ANSYS CTO), Ion Stoica (Databricks Founder), Jared Kushner (former Senior Advisor to the President), David Patterson (Turing Award Winner), and Luis Videgaray (former Foreign and Finance Minister of Mexico).

As a founding Geophysics + Seismic Researcher, you'll be in the arena from day one, at the exact intersection where deep learning meets subsurface physics. This is your chance to take everything you've learned across seismic processing, interpretation, and geologic modeling and use it to build foundation AI models that transform how the energy and subsurface characterization industries work.

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: manual and compute-intensive seismic interpretation, slow full waveform inversion, and the disconnect between geophysical models and the petrophysical properties that actually drive decisions.

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  Develop novel AI architectures for subsurface physics, including transformer-based and and generative neural operators for seismic modeling, inversion, and geologic interpretation

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  Own research workflows end to end: synthetic data generation (procedural subsurface/geology models, forward simulation), model training, validation, and deployment

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  Apply statistics and uncertainty quantification, including Bayesian and generative modeling, to represent multiple plausible subsurface realizations rather than a single deterministic answer

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  Work hands-on with seismic processing, full waveform inversion (FWI), and geologic modeling, and help connect velocity/density models to the petrophysical properties (porosity, permeability, lithology) that matter for reservoir and geologic decisions

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  Run large-scale experiments on seismic and geologic datasets, iterate rapidly on model performance, and help drive the research roadmap based on what actually works

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  Collaborate directly with domain experts and customers in energy, subsurface characterization, and related industries to understand workflows, pain points, and validation criteria

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  Publish and present breakthrough results, internally and externally, as we push the boundaries of AI for subsurface physics

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  Move fast and ship: take research from idea to production-ready model in weeks, not months, and see your work deployed to real customers

• PhD in Geophysics or a related engineering discipline, with extensive experience in seismic processing and interpretation

• Strong AI background, including models for physical systems such as FNO and Transolver

• Experience in statistics and uncertainty quantification; familiarity with seismic interpretation, full waveform inversion, and geologic modeling

• Proficient in Python and ML frameworks, able to independently own workflows from synthetic data generation through model training

• Published research in top-tier geophysics or ML venues (e.g., Geophysics, JGR, NeurIPS, ICML, ICLR)

• Experience with generative modeling for geology (e.g., 3D subsurface realizations) and geostatistics (Gaussian processes, kriging)

• Background in adjacent physical-systems or earth-science modeling domains (e.g., oceanography, geospatial science, other geophysical inversion problems)

• Large-scale distributed training experience with multi-GPU or multi-node setups

• Experience at high-growth AI startups (Seed to Series C) or leading research labs

• Comfortable across Python, Julia, and modern ML tooling; open-source contributions to geophysics or ML codebases

• Forward-deployed experience working directly with customers to solve their hardest problems

“The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again... who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly.” - Teddy Roosevelt

At our core, we believe in being “in the arena.” We are builders, problem solvers, and risk-takers who show up every day ready to put in the work: to sweat, to struggle, and to push past our limits. We know that real progress comes with missteps, iteration, and resilience. We embrace that journey fully knowing that daring greatly is the only way to create something truly meaningful.