What are the responsibilities and job description for the Junior AI Scientist position at SLB?
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).
- Education & Experience
- 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.