Geophysicist, Seismic Inversion; Senior - Principal - Advisor) Landmark

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Date:
Sep 21, 2026

Location:

Houston, TX, US, 77032

We are looking for the right people — people who want to innovate, achieve, grow and lead. We attract and retain the best talent by investing in our employees and empowering them to develop themselves and their careers. Experience the challenges, rewards and opportunity of working for one of the world’s largest providers of products and services to the global energy industry.

Landmark, a Halliburton business line, provides the industry’s most comprehensive suite of digital solutions for exploration, drilling, and production optimization. Its software and data platforms empower customers to model subsurface assets, manage drilling risk, and accelerate decision-making through cloud, AI, and advanced analytics.

We are seeking an R&D geophysicist to develop advanced seismic imaging, inversion, and quantitative-interpretation technologies for reservoir characterization. The role will apply Python, machine learning, deep learning, and mathematical inversion methods to integrate seismic data with well logs, rock physics, petrophysics, sequence stratigraphy, and seismic facies information.

You will develop and validate workflows that transform seismic observations into high-resolution 3D estimates of reservoir properties such as porosity, shale volume, and fluid saturation, including associated uncertainty. Working with geoscientists, reservoir engineers, data scientists, software developers, product stakeholders, and technical customers, you will convert research concepts into tested prototypes and product-ready technical capabilities.

• Develop elastic full-waveform inversion and acoustic or elastic seismic-inversion methods for shot-gather, post-stack, and pre-stack AVO/AVA applications.
• Design AI and deep-learning approaches that improve seismic imaging, inversion, seismic facies classification, and seismic-to-petrophysical property mapping.
• Integrate seismic volumes, well logs, core measurements, rock-physics relationships, and geologic interpretations to construct high-resolution 3D reservoir-property models.
• Develop Python-based research prototypes, algorithms, and reproducible technical workflows using appropriate scientific-computing and machine-learning frameworks.
• Quantify uncertainty in predicted rock properties and fluid saturations using statistical, probabilistic, ensemble, or AI-enabled inverse-problem methods.
• Distinguish and evaluate epistemic and aleatoric uncertainty, analyze model generalization, and identify limitations in training data and model assumptions.
• Validate seismic, reservoir, and machine-learning models using blind-well tests, held-out data, physical consistency checks, and comparison with known subsurface observations.
• Incorporate sequence-strati graphic frameworks, seismic facies, geostatistical principles, and petrophysical constraints into reservoir-characterization workflows.
• Collaborate with multidisciplinary R&D and product teams to define technical problems, prioritize experiments, assess feasibility, and translate successful research into usable software capabilities.
• Document methods, assumptions, experiments, results, model limitations, and recommendations, and communicate complex findings to technical and product stakeholders.
• Monitor advances in geophysics, AI, deep learning, and scientific computing, and contribute to technical publications, presentations, invention disclosures, and patents where appropriate.

• Undergraduate degree in Science, Engineering, or a similar technical discipline.
• Minimum of four years of related experience in applied research, technology development, product development, engineering, scientific analysis, or a comparable technical field.
• Demonstrated experience applying scientific or engineering principles to seismic imaging, seismic inversion, quantitative interpretation, reservoir characterization, or a closely related technical problem.
• Proficiency in Python and experience developing scientific-computing, data-analysis, machine-learning, or deep-learning workflows.
• Experience designing technical investigations, experiments, model evaluations, or validation studies and interpreting the resulting data.
• Ability to document technical work, manage defined R&D deliverables, and communicate complex concepts and recommendations to multidisciplinary technical stakeholders.

• Master’s degree or PhD in…