Terra AI
Full Stack Engineer, Scientific Modeling Tools
US remote
Sponsorship not specifiedDetected 180 days ago
PythonC++Full-Stack DevelopmentMachine LearningTensorFlowPyTorchCFDCommunicationReservoir Engineering
About the role
- By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise, Terra AI helps exploration and mining companies make faster, more informed subsurface decisions with greater confidence and capital efficiency.
- ROLE Productionize and extend internal modeling tools used to generate subsurface outputs.
- You will take software built around scientific workflows and make it robust, maintainable, and easier to run, inspect, and extend.
Responsibilities
- Collaborate closely with domain experts to translate requirements into software that is correct, usable, and extensible.
- Design and implement APIs and interfaces that turn working examples into maintainable components.
- Build configuration management patterns that make runs reproducible and debuggable.
- Implement and maintain orchestration pipelines for simulation ensembles and data validation.
Requirements
- Strong software engineering fundamentals and proven ability to take ownership of complex codebases.
- Comfort working in Julia or willingness to go deep quickly.
- Familiarity with performance profiling and optimization tooling.
- Experience with orchestration or workflow tooling (Flyte, Prefect, Dagster, or similar), or equivalent patterns built in-house.
Nice to have
- Geophysics or geomodeling experience, including survey simulation or related tooling (SimPEG or similar).
- Reservoir simulation experience (Eclipse, Intersect, JutulDarcy, or similar).
- Experience solving PDE-based problems in HPC environments.
- Familiarity with Fortran or C++ codebases common in scientific stacks.
- Experience in simulation, CAD, CFD, or other engineering/scientific software domains.
- Experience supporting scientific users and workflows, where communication and shared language matter.
- Experience with batch pipelines and data-intensive systems.
- Familiarity with ML frameworks at an integration level (PyTorch preferred, TensorFlow or JAX also relevant), including artifacts, I/O, and runtime concerns.
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This listing is sourced directly from Terra AI's careers page and normalized into a canonical job model.