AI Integration Engineer

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.

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