The company is looking to build, train and deploy large-scale, self-supervised foundation models that learn rich representations of seismic data to be fine-tuned for tasks such as event detection, subsurface imaging, fault characterization or reservoir property estimation.
Requirements
- Seismic theory and processing: data formats (SEG-Y/D), de-noising, deconvolution, stacking, migration, tomography, inversion
- Reservoir geomechanics, rock physics, well-log integration, AVO/AVA analysis
- Geostatistics: variography, kriging, co-kriging, uncertainty quantification
- Self-supervised and semi-supervised learning: masked autoencoders (MAE), contrastive methods (SimCLR, BYOL), clustering-based (DINO), predictive coding
- Model architectures: 1D/2D/3D CNNs, Vision/Audio Transformers, graph neural networks, diffusion/generative models, multi-modal encoders
- Deep-learning frameworks: PyTorch (Lightning, Distributed), TensorFlow/Keras, JAX/Flax
- Large-scale training: multi-GPU, multi-node, mixed-precision, ZeRO optimization
Responsibilities
- Build, train and deploy large-scale, self-supervised foundation models that learn rich representations of seismic data
- Fine-tune models for tasks such as event detection, subsurface imaging, fault characterization or reservoir property estimation
- Apply domain knowledge of seismic theory and processing, reservoir geomechanics, rock physics, and geostatistics
- Utilize machine-learning and foundation-model expertise, including self-supervised and semi-supervised learning, model architectures, and transfer learning
- Implement software and infrastructure, including programming in Python and C++/CUDA, deep-learning frameworks, and large-scale training
- Apply mathematical and algorithmic foundations, including linear algebra, probability and statistics, optimization, and signal processing
- Collaborate with geoscientists, software engineers, product managers, and end-users to present complex model behaviors and uncertainty quantification
Other
- PhD (or M.S. + 5+ years) in Geophysics, Seismology, Computer Science, Electrical Engineering, Applied Math, or equivalent
- 2–3+ years hands-on seismic/geophysical data processing or interpretation
- Peer-reviewed publications or patents in seismic AI, geophysical inversion or related fields a plus
- Cross-disciplinary teamwork with geoscientists, software engineers, product managers and end-users
- Clear presentation of complex model behaviors, uncertainty quantification and business impact
