Computer Vision Software Engineer, Mid

Booz Allen Hamilton•Dayton, OH

1d•Remote

About The Position

Join a mission‑focused engineering team building advanced computer vision (CV), multi‑sensor fusion, and detection and tracking capabilities for remote sensing and GEOINT environments. As a Mid‑Level Computer Vision Engineer, you will design, implement, and optimize CV algorithms and deep learning pipelines, contributing to operational systems used across national security-related missions. You will work closely with senior engineers while owning well‑defined components of the technical solution.

Requirements

3+ years of experience developing computer vision algorithms for detection, tracking, or sensor‑based analytics in remote sensing or GEOINT‑relevant environments
1+ years of experience applying deep learning to CV problems using transformer‑based, self‑supervised, or contrastive learning architectures, such as DINO, CLIP, or SAM
Experience building pipelines in Python or C++ for algorithm development, training, evaluation, or deployment
Experience with GPU‑accelerated workflows
Experience with classical tracking and estimation methods, such as Kalman or extended Kalman filters, supporting real‑time development needs
Active TS/SCI clearance; willingness to take a polygraph exam
Bachelor's degree in a STEM field

Nice To Haves

Experience in GPU programming, including CUDA or RAPIDs
Experience with developing synthetic modeling of Kinematic target features
Experience with AI‑augmented development workflows or agentic tools, such as Codex, Claude Code, or OpenCode or other multi-agent approach
Knowledge of modern software design patterns, including micro-service design and orchestration in Kubernetes deployment
Master’s degree in Computer Science, Electrical Engineering, Computer Engineering, AI, ML, Physics, Mathematics or a related field

Responsibilities

Develop and implement computer vision algorithms for target detection, characterization, and tracking across single‑ and multi‑sensor data.
Build and maintain model training, evaluation, and deployment pipelines in Python or C++.
Apply deep learning approaches, including transformer‑based architectures such as DINO, CLIP, or SAM, to image understanding problems.
Integrate classical estimation methods, such as Kalman‑family filters with modern deep learning workflows to support real‑time algorithm development.
Contribute to GPU‑accelerated model development using CUDA, RAPIDS, or vendor‑provided inference runtimes.
Collaborate with multidisciplinary engineering teams to test, refine, and operationalize computer vision models for constrained compute or real‑time environments.
Support the implementation of microservice‑based inference pipelines or containerized model deployments under senior guidance.
Contribute to the architecture and implementation of novel single and multi-sensor platform detection and tracking algorithms and track fusion of targets.