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# Machine Learning Expert

- **Company:** Occidental Petroleum
- **Location:** Houston, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Artificial Neural Networks, Cloud Computing, Data Cleansing, Software Debugging, Distributed Systems, Python (Programming Language), Machine Learning, Pytorch, Uncertainty Quantification (UQ), Deep Learning, Information Technology
- **Published:** October 27, 2026
- **Apply:** https://www.adzuna.com/details/5879720998

## About the Role

* M.S. or Ph.D. in Geophysics, Applied Mathematics, Physics, Computer Science, or a related quantitative field.
 * Minimum 5 years of hands-on (post academic) FWI experience: you have personally run full-waveform inversion on real seismic data. Familiarity with FWI from coursework or literature alone is not sufficient for this role.
 * Demonstrated experience applying machine learning to geophysical or other physics-based scientific problems.
 * Working knowledge of seismic imaging and the numerical methods behind it: wave propagation, inverse problems, and optimization.
 * Proficiency in Python and hands-on experience with PyTorch, including training, validation, and performance evaluation.
 * Experience working with large scientific datasets and computational workflows.
 * Sound scientific judgment: the ability to design a fair comparison, recognize a result that is too good to be true, and communicate uncertainty honestly.

Additional Desired Qualifications:

 * Experience with neural operators, implicit neural representations, generative models, or physics-informed neural networks applied to wave-equation problems.
 * Experience combining wave physics with ML, including differentiable modeling and inversion.
 * Uncertainty quantification, surrogate modeling, or ML-assisted optimization for expensive geophysical workflows.
 * GPU, HPC, or distributed computing experience; AWS in particular.
 * Experience with offshore or Gulf of America seismic data.
 * Effective use of AI-assisted coding tools to accelerate development and debugging of scientific workflows.
 * A record of research prototypes that reached production use.

## Description

* Develop ML and hybrid physics-ML methods for seismic imaging, inversion, and velocity model building - including ML-assisted RTM, LSRTM, and FWI.
 * Apply deep learning, generative models, neural operators, and physics-informed approaches to problems such as denoising, interpolation, image enhancement, model prediction, uncertainty estimation, and automated interpretation.
 * Build workflows that handle large multidimensional seismic datasets on GPU and cloud infrastructure.
 * Validate new methods against established physics-based approaches, and make an evidence-based case for where they add value and where they do not.
 * Move successful prototypes into production imaging workflows, with the documentation and reproducibility that requires.
 * Communicate results clearly to geophysicists, data scientists, and asset teams.