Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Houston, Texas
Live, first seen by us 56 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
Occidental does not offer sponsorship of employment-based nonimmigrant visa petitions for this role
Develop ML and hybrid physics-ML methods for seismic imaging, inversion and velocity model building
Apply deep learning and generative models to denoising, interpolation and automated interpretation
Build workflows for large multidimensional seismic datasets on GPU and cloud infrastructure
Validate new methods against established physics-based approaches
Move successful prototypes into production imaging workflows with documentation
Develop ML and hybrid physics-ML methods for seismic imaging, inversion, and velocity model building
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
Move successful prototypes into production imaging workflows, with the documentation and reproducibility that requires.
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.
Experience with neural operators, implicit neural representations, generative models, or physics-informed neural networks applied to wave-equation problems.
Demonstrated experience applying machine learning to geophysical or other physics-based scientific problems.
Experience combining wave physics with ML, including differentiable modeling and inversion.
Proficiency in Python and hands-on experience with PyTorch, including training, validation, and performance evaluation.
Experience with offshore or Gulf of America seismic data.
Qualifications: - 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.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Day 56
Similar jobs usually come down between day 6 and day 70.
From 4,444 similar postings that came down. Coming down is not always a hire.
4 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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.
- 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.
Complete your application on oxy.wd5.myworkdayjobs.com. The employer’s form will show what is required.
Already applied? Track this application
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Oxy produces, markets and transports oil and natural gas to maximize value and provide resources fundamental to life. The company leverages its global leadership in carbon management to advance lower-carbon technologies and products. Headquartered in Houston, Oxy primarily operates in the United States, Middle East and North Africa. To learn more, visit Oxy
Oxy strives to attract and retain talented employees by investing in their professional development and providing rewarding opportunities for personal growth. Our goal is to meet the highest employer standards by ensuring the health and safety of our employees, protecting the environment and positively impacting our communities where we do business.
We are looking for a motivated individual to fill the position of Machine Learning Expert within our Subsurface Innovation Imaging Team based in Houston, TX.
Essential Job Responsibilities:
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.
Qualifications:
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.
Occidental does not offer sponsorship of employment-based nonimmigrant visa petitions for this role.
Recruitment Fraud
It has come to our attention various individuals and/or organizations are contacting people falsely pretending to recruit on behalf of Oxy. Please be aware that these recruiting scams and communications do not originate nor are they associated with our recruitment process. All Oxy job postings and offers will require a completed application through our company website.
Oxy does not charge a fee at any stage of the recruiting process. We will never:
• Ask you to pay for applications, interviews, meetings, processing, training or for any other fees
• Use recruiting or placement agencies that charge candidates an advance fee of any kind or
• Request personal information such as passport and bank account details at an early stage of our recruitment process.
We recommend against responding to unsolicited business propositions or offers from people you don't know. Do not disclose your personal or financial details. If you believe you have been the victim of a recruiting scam, please contact your local police department.
All qualified applicants will receive consideration for employment without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
No pay amount identified in the saved description.
Houston, Texas
Essential Job Responsibilities: - 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.
This passage needs a closer read in the full description.