Computer Vision Software Engineer, Mid

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 missions.

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

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

Qualifications

• 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 microservice design and orchestration in Kubernetes deployment
• Master’s degree in Computer Science, Electrical Engineering, Computer Engineering, AI/ML, Physics, Mathematics or other related field

Benefits

• Health, life, disability, financial, and retirement benefits
• Paid leave, professional development, tuition assistance, work-life programs, and dependent care
• Recognition awards program for exceptional performance and superior demonstration of company values

Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits.

Pay

The projected compensation range for this position is $69,300.00 to $158,000.00 (annualized USD). Salary is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements.

Schedule

This posting will close within 90 days from the posting date.

Work model expectations:

• Remote: Generally virtual with occasional in-person work at a Booz Allen or customer facility.
• Hybrid: Frequent work from a Booz Allen facility, with potential customer facility visits as required by the role.
• Onsite: Primarily performed at a Booz Allen office or customer facility for direct collaboration.

Employees working virtually are generally expected to have their cameras on during meetings to support engagement and effective communication.