Computer Vision Software Engineer, Mid

Booz Allen Hamilton; Dayton (OH); On-site; USD 69,300 - 158,000; Full time

Benefits offered by this job

Health benefits

Tuition assistance

Professional development programs

Paid leave

Job summary

Booz Allen Hamilton is looking for a Mid-Level Computer Vision Engineer in Dayton, Ohio. The ideal candidate will design and optimize computer vision algorithms for remote sensing and GEOINT missions, contributing to systems that support national security operations.

You will collaborate with multidisciplinary teams, applying your expertise in Python or C++ and deep learning methods to drive innovations in detection and tracking capabilities. This role requires a strong background in computer vision and relevant experience in developing algorithms under real-time constraints.

Qualifications

• 3+ years of experience developing computer vision algorithms for detection and tracking.
• 1+ years of experience applying deep learning to CV using transformer-based architectures.
• Experience building pipelines in Python or C++ for algorithm development.

Responsibilities

• Develop and implement computer vision algorithms for target detection and tracking.
• Build and maintain model training and deployment pipelines.
• Apply deep learning approaches to image understanding problems.

Skills

Computer Vision Algorithms

Deep Learning

Python

C++

GPU Programming

Kalman Filters

Education

Bachelor's degree in a STEM field

Tools

CUDA

RAPIDS

Job description

Your growth matters to us - explore our career development opportunities.

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Connect with others in our people-first culture and enhance our collective ingenuity.

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YOUR CANDIDATE JOURNEY

Discover what to expect during your journey as a candidate with us.

THE OPPORTUNITY

Join a mission‑focused engineering team building advanced computer vision, 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. You will work closely with senior engineers while owning well‑defined components of the technical solution.

What You’ll Work On

• 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

You Have

• 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 degreein a STEM field

Nice If You Have

• 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 degreein Computer Science, Electrical Engineering, Computer Engineering, AI/ML, Physics, Mathematics or other related field

Clearance

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.

Compensation

Salary at Booz Allen is determined by various factors, including location, the individual’s education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $69,300.00 to $158,000.00 (annualized USD). This posting will close within 90 days from the Posting Date.

Benefits

At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well‑being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work‑life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our 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.

EEO Statement

All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.