Computer Vision Software Engineer, Lead

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Learn the skills you need to accelerate your career.

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Build mission-ready tech that protects the nation.

As a Senior Computer Vision Engineer, you will design, develop, and optimize advanced computer vision and multi‑sensor fusion algorithms supporting GEOINT‑mission workflows. You will lead the development of deep learning models, real‑time tracking systems, and GPU‑accelerated pipelines deployed in operational environments. You will shape next‑generation tools that integrate imaging physics, ML‑based behavior inference, and multi‑sensor fusion.

You’ll join a collaborative technical delivery team where you’ll contribute to secure, reliable, and user‑centric tools that accelerate mission outcomes.

Develop and implement deep learning computer vision models, with a focus on sensor fusion and target tracking.

Collaborate with multidisciplinary teams to design, develop, test, and deploy technical solutions in Python or C++.

Utilize GPU programming, including CUDA or RAPIDs, to optimize the performance of computer vision applications.

Contribute to the architecture and implementation of novel single and multi‑sensor detection and tracking and fusion of targets.

Join us. The world can’t wait.

6+ years of experience developing computer vision algorithms for detection, tracking, or multi‑sensor fusion in remote sensing or GEOINT environments

2+ years of experience applying deep learning to computer vision problems using transformer‑based or self‑supervised architectures

Experience implementing model pipelines in Python or C++, including training, evaluation, and deployment workflows

Experience with administration of continuous integration or continuous deployment ( CI / CD ) pipelines using Kubernetes, Docker, or Jenkins

Experience with Agile met hodology, extreme programming, sof tware engineering, product management, and sof tware products, and acquiring client requirements and resolving workflow problems through automation optimization

Knowledge of GPU‑accelerated development using CUDA, RAPIDS, or GPU programming frameworks

Knowledge of classical tracking or estimation met hods such as Kalman or extended filters, to support real‑time algorithm development

Ability to design, test, and optimize algorithms for operational performance in constrained computing environments such as multi‑GPU servers or cloud

Active TS/SCI clearance; willingness to take a polygraph exam

Bachelor’s degree in a STEM field

Experience with GPU‑accelerated deep learning, including CUDA kernel development, TensorRT optimization, RAPIDS, or distributed multi‑GPU training

Experience developing synthetic data, kinematic target models, or scenario simulation tools to support algorithm training or evaluation

Experience with advanced estimation, tracking, and fusion techniques such as joint multi‑sensor registration, Bayesian fusion, particle filters, or deep multi‑object tracking pipelines

Experience with MLOps or scalable deployment systems, including Docker, Kubernetes, ONNX Runtime, or Triton Inference Server

Experience building or optimizing microservice architectures or distributed systems for real‑time data processing

Experience integrating CV models into edge, embedded, or latency‑constrained operational environments

Experience with transformer‑based vision architectures beyond DINO, CLIP, or SAM such as ViT variants or self‑supervised multi‑modal encoders

Experience with cloud ML platforms such as AWS Gov Cloud, Azure ML, or on‑premises GPU clusters

Knowledge of geospatial data formats, sensor phenomenology, or remote ‑sensing exploitation workflows

Master’s degree in CS, Electrical Engineering, Computer Engineering, AI / ML, Physics, or Mathematics preferred ;
Doctorate degree in CS, Electrical Engineering, Computer Engineering, AI / ML, Physics, or Mathematics a plus

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information ; TS/SCI clearance is required.