Software Engineer, Senior

GRVTY•Herndon, VA

11d•Onsite

About The Position

GRVTY is looking for a Senior Software Engineer to join a small, technically focused team supporting national security missions at a customer site in Herndon, Virginia. This is hands-on engineering work — you will be designing and building Python-based software tools that support optical signature modeling, spectral data analysis, and machine learning-enabled sensor workflows. The work spans algorithm development, data pipeline construction, and integration of ML capabilities into operational and analytical environments. You will work directly with scientists, sensor domain experts, and intelligence analysts — translating complex technical requirements into functional, maintainable software. This is not a role for someone looking to coast. We want someone who is technically sharp, can operate with a degree of autonomy, and understands that the software they build has real downstream impact on mission outcomes.

Requirements

Active TS/SCI clearance with CI polygraph.
Bachelor's degree in Computer Science, Engineering, Physics, Mathematics, Data Science, or a related STEM field. Equivalent hands-on experience will be considered.
9+ years of professional software development experience, with demonstrated focus on scientific computing, algorithm development, data processing, or related technical domains or 7+ years of experience with a Masters degree
Expert-level Python programming, including object-oriented design, modular architecture, and production-quality code.
Experience developing software for data-intensive workflows — sensor data, scientific data, simulation, or equivalent.
Ability to work from technical or scientific requirements and produce well-structured software architecture, logic flows, and implementation plans.
Comfortable operating in both Windows and Linux environments, including secure or classified customer workspaces.
Familiarity with source control and development tooling: Git, GitLab, Bitbucket, Jira, Jenkins, or equivalent.
Strong communication skills — able to work directly with domain experts who are not software engineers and translate what they need into software that actually works.

Nice To Haves

Experience with optical signature modeling, EO/IR sensor data, spectral analysis, or hyperspectral data processing.
Familiarity with machine learning frameworks and workflows: PyTorch, TensorFlow, scikit-learn, or equivalent.
Experience with scientific Python libraries: NumPy, pandas, OpenCV, SciPy, or similar.
Background in signal processing, image processing, or remote sensing data workflows.
Experience with simulation, modeling, or mission analysis software in a defense or intelligence context.
Familiarity with ML/Ops practices — training pipelines, data labeling, model evaluation, and deployment workflows.
Experience integrating algorithms into existing operational software suites.
Proficiency in a secondary technical language: C, C++, MATLAB, Java, or Rust.
Advanced degree in a relevant technical field (Computer Science, Electrical Engineering, Physics, Image Science, or related).
Prior experience supporting defense, intelligence, or classified customer environments.
Experience with CI/CD practices, automated testing, and DevSecOps tooling.

Responsibilities

Design, develop, and maintain Python-based software tools supporting optical signature modeling, spectral sensor data processing, and analytical workflows.
Work with sensor physicists, data scientists, and machine learning engineers to translate technical concepts and mission requirements into working software.
Build and optimize data pipelines for ingesting, processing, and analyzing large-scale scientific and sensor datasets.
Develop and integrate machine learning workflows — including training data preparation, model integration, and evaluation — into operational toolsets.
Create clear software architecture documentation, workflow diagrams, and code structure so tools can be maintained, extended, and transitioned effectively.
Test, validate, and troubleshoot algorithms and software modules across development and deployment environments, including classified workspaces.
Manage and maintain Git repositories with discipline — clean commits, meaningful documentation, and reproducible builds.
Contribute to a culture of technical rigor: peer reviews, coding standards, and honest engagement with complex problems.