SLB

Compensation

Full Description

Build, train and deploy large-scale, self-supervised “foundation” models that learn rich representations of seismic data (ND(2D,3D, ) volumes), to be fine-tuned for tasks such as event detection, subsurface imaging, fault characterization or reservoir property estimation. Domain Knowledge Seismic theory & 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. Machine-Learning & Foundation-Model Expertise 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. Transfer learning & fine-tuning at scale: prompt/adapter-based techniques, domain adaptation. Evaluation metrics: geophysical error norms (L2, semblance), detection/segmentation metrics (IoU, F1), end-use KPIs (horizon-picking accuracy, attribute classification). Software & Infrastructure Programming: expert Python (NumPy, SciPy, Pandas), C++/CUDA for performance kernels. Deep-learning frameworks: PyTorch (Lightning, Distributed), TensorFlow/Keras, JAX/Flax. Large-scale training: multi-GPU, multi-node, mixed-precision, ZeRO optimization. Data engineering: advanced segmentation of terabyte-scale seismic volumes. Mathematical & Algorithmic Foundations Linear algebra, probability & statistics, optimization (stochastic, convex, non-convex). Signal processing: Fourier/Wavelet transforms, filtering, spectral analysis. Numerical methods for PDEs, inverse problems, regularization techniques. Collaboration & Communication Cross-disciplinary teamwork with geoscientists, software engineers, product managers and end-users. Clear presentation of complex model behaviors, uncertainty quantification and business impact. Desired Extras Contributions to open-source seismic/ML projects or standards bodies (e.g. SEG, OSDU). Cloud & DevOps: AWS/GCP/Azure (S3, EC2/GPU, Batch/ML Engine), Kubernetes, Terraform, Docker, CI/CD pipelines. Experiment tracking & MLOps: MLflow, Weights & Biases, Neptune, Grafana, Prometheus. Multi-modal fusion: combining seismic with well logs, production data, satellite/inSAR. Agile/Scrum practices: sprint planning, peer code reviews, documentation (API specs, best practices).