AI/ML Engineer, Senior

GRVTY•Springfield, VA

21h•$200,000 - $240,000

About The Position

GRVTY is looking for a Senior AI/ML Engineer to support the design, development, and operational deployment of machine learning capabilities applied to sensor data in direct support of national security missions. This is a technically demanding role; we are not looking for someone who has applied pre-built models to standard datasets. We need someone who understands the underlying mechanics of how modern ML architecture work, can make principled decisions about which approaches are appropriate for a given problem, and can implement them correctly from the ground up in a mission context. The work centers on using AI and ML techniques to detect, classify, and characterize events and phenomena within sensor data; translating complex phenomenology into automated, scalable analytical capabilities. You will work directly with scientists, algorithm engineers, and intelligence analysts to understand the problem space, design the right solution, and deliver operational-prototype code that runs reliably in classified R&D environments. You will also serve as a technical anchor for junior developers on the team, setting standards, reviewing work, and helping others grow.

Requirements

Active Top Secret Clearance with the ability to obtain SCI and CI Polygraph
Bachelor's degree in Computer Science, Electrical Engineering, Physics, Mathematics, Data Science, or a closely related technical field. Advanced degree strongly preferred.
7+ years of hands-on experience in machine learning, with a demonstrable track record of developing and deploying models in real operational or production environments, not just research prototypes.
Deep, working knowledge of modern ML architectures, CNNs, transformers, object detection frameworks, and related methods, at the level where you understand why they work, where they break, and how to adapt them to non-standard data domains.
Expert-level Python programming, including experience with PyTorch, TensorFlow, or equivalent frameworks at production scale.
Experience building complete ML pipelines: data ingestion and preprocessing, annotation and labeling strategy, training, evaluation, and deployment.
Demonstrated ability to work directly with domain scientists or subject matter experts, translating technical requirements into ML design, and ML outputs back into domain-meaningful terms.
Experience mentoring or leading junior developers in a hands-on technical capacity, not just oversight, but active code review, standard-setting, and knowledge transfer.
Strong documentation and communication skills; capable of producing technical reports and briefing findings to non-ML audiences.

Nice To Haves

Experience applying ML to senor data that includes EO/IR, hyperspectral, SAR, or other remote sensing sensor data — familiarity with the phenomenology matters here, not just the algorithms.
Background in computer vision, image classification, anomaly detection, or event characterization in scientific or sensor data contexts.
Experience training models on simulated or synthetic data — understanding the gap between simulated training environments and real-world operational data, and how to manage it.
Familiarity with ML/Ops practices: experiment tracking, model versioning, reproducible pipelines, and deployment in constrained or classified environments.
Experience working with large-scale geospatial or scientific datasets; comfort with data that is noisy, sparse, or domain-specific.
Proficiency with scientific Python libraries: NumPy, SciPy, pandas, OpenCV, scikit-learn, or equivalent.
Experience with cloud-based ML training environments (AWS, Azure, or equivalent) and GPU-accelerated compute.
Prior experience supporting defense, intelligence, or classified customer environments.
Ph.D. in a relevant quantitative field — Astronomy, Physics, Computer Science, Electrical Engineering, or related discipline — particularly where the doctoral work involved applied ML on real scientific data.

Responsibilities

Design and implement machine learning models for detection, classification, and characterization of events and signatures within sensor data, with enough architectural depth to select and tune the right approach rather than default to the nearest available framework.
Develop end-to-end ML workflows: training data preparation, model development, evaluation, optimization, and deployment into operational or near-operational environments.
Work closely with scientists and domain experts to understand sensor phenomenology, data characteristics, and mission requirements, translating that knowledge into sound ML design decisions.
Write production quality Python code that is modular, well-documented, testable, and maintainable by others, not just scripts that work in a notebook.
Evaluate model performance rigorously against operationally meaningful metrics; identify failure modes, characterize edge cases, and iterate systematically.
Integrate ML capabilities into existing analytical pipelines and software environments; own that integration through to deployment.
Mentor junior AI/ML developers, review code, establish team-wide standards, identify gaps in understanding, and actively develop the technical capability of those around you.
Contribute to trade studies and architecture decisions; provide technically grounded recommendations on ML approaches, tooling, and implementation strategies.
Prepare and deliver technical briefings and written documentation to customer and program stakeholders at a level of clarity that holds up to scrutiny.