Senior Software Engineer – Computational Algorithms

About GeoSoftware

GeoSoftware is a global leader in Geoscience Software and provides a comprehensive range of software solutions that support our clients to achieve their goals. Our tools help our clients to develop a better understanding of the subsurface, manage exploration and development risks, and quantify reserves more accurately with our advanced geoscience technology.

Working at GeoSoftware is all about passion and sharing our ambition for ongoing technological innovation. We have more than 150 employees, spread over 18 different countries. Our people are our most important asset and developing our talents is a priority. As part of GeoSoftware, you will work with international and diverse teams of experts in an environment of excellence, which will enable you to create your own opportunities and realize your full potential.

Job Summary

We are looking for an experienced computational engineer to join our Digital Platform team.

You would work on the numerical core of our geoscience software: algorithms that have been in production use across the industry for two decades, which we are modernizing into headless, independently testable, independently deployable components that run correctly and quickly across hundreds of cores.

You would write the tests that show an optimized version still gives the same answer.

This is as much a research role as an engineering one. A large part of it is finding the faster way: reading the literature, questioning an algorithm's formulation rather than only its implementation, and knowing when an order-of-magnitude gain is sitting in the mathematics rather than in the loop. The other part is working directly with subject matter experts—building quick prototypes with them, putting something in front of them within days, and iterating on it until the science is right, before any of it is hardened into production code.

We are looking for a physics or geoscience background and strong Python alongside C++.

You would work in a small, senior, distributed team alongside platform and front-end engineers, and with geoscientists who use the results professionally every day.

Duties and Responsibilities

Algorithm Development and Modernization

Separate established numerical code from the application framework it grew up in, producing headless, testable, independently deployable components
Work inside a large, mature, sparsely documented C++ codebase and leave it better understood than you found it
Define each algorithm's interface—its inputs, outputs and parameters—precisely enough that the surrounding platform can validate, present and re-run it without knowing what the algorithm does
Expose C++ to Python through pybind11, with attention to memory ownership, the GIL and zero-copy array handling

Research, Prototyping, and Optimization

Investigate how an algorithm can be made substantially faster — reformulation, better numerical methods, exploiting structure in the problem — not only tuning the code that implements it
Follow the relevant literature, and evaluate published approaches honestly against what we already have
Build quick prototypes in Python with subject matter experts, iterate with them on the science, and only then decide what deserves to become production C++
Profile and optimize: vectorization, cache behavior, memory layout, threading and choice of numerical library
Decompose algorithms for parallel execution, and reason rigorously about what that does to floating-point reproducibility
Work with the platform engineers on how compute is distributed and on the seam between native code and the execution layer

Verification

Build the regression harness that validates parallel and distributes output against the single-threaded reference
Define and justify the acceptance tolerance for each algorithm family
Write characterization tests before refactoring and regression suites that catch what code review will not

Collaboration and Delivery

Work in short iterations with continuous integration, code review and acceptance criteria agreed before implementation
Work with geoscience domain experts to confirm that a result is not only numerically equal but scientifically sound
Contribute to the technical direction of a small team, and write down the decisions that matter

Skills and Competencies

Skills:

Strong modern C++ with real numerical computing experience—linear algebra, signal processing, transforms, optimization, or inversion
Strong Python, including the scientific stack—NumPy, SciPy, and the array-programming idiom—and experience binding native code to it
A physics, geophysics, or applied mathematics grounding and the ability to read a paper and implement what it describes
Solid grounding in floating-point behavior, numerical stability, and error analysis
Parallel programming: threading, SIMD, and the trade-offs of data decomposition
Ability to work productively inside a large, unfamiliar, poorly documented codebase
Prototyping speed: able to get something working and demonstrable in front of a domain expert quickly and comfortable throwing it away
A test-first instinct and the patience to establish a baseline before changing anything
Strong written English and the habit of documenting asynchronously

Competencies:

Rigour, and comfort with being held to a numerical standard
Ownership and self-direction
Pragmatic judgement about where complexity is worth it
Collaboration across disciplines
Technical expertise (numerical and scientific computing)
Alignment with company interest

Qualifications & Requirements

Required:

Degree in physics, geophysics, geoscience, applied mathematics, or a closely related quantitative discipline, or demonstrable equivalent depth in a quantitative discipline. A postgraduate degree is welcome but not required
Minimum 5 years of professional experience in numerical, scientific or performance-critical computing
Strong Python, including the scientific stack, and experience at the native-to-Python boundary
Demonstrable modern C++ experience in a production codebase
Demonstrable experience with parallel programming and performance optimization
A genuine interest in making algorithms faster and in the research that leads there
Experience working directly with scientists or other domain experts and prototyping collaboratively with them
Able to work effectively in a small, remote, multi-time-zone team: writing things down, communicating asynchronously, and operating without close supervision
Fluent written and spoken English

Desired:

Geophysics or seismic processing background
Published research, or a research background in a computational discipline
Experience with inversion, deconvolution, transforms, or wavelet estimation
Distributed compute frameworks—Dask, Ray, or MPI—or GPU computing
Performance work with BLAS, LAPACK, FFTW, or Eigen
Petrophysics or well-log computation
Cloud-native, chunked array storage formats
Experience extracting library code out of a monolithic application
Fluent with AI coding tools for prototyping, test generation, and refactoring, with the judgement to know where their output must be verified rather than trusted

We value the unique ways in which we differ, and we are committed to equal employment opportunities for all professionals.