Computer Vision Software Engineer, Lead

Booz Allen Hamilton

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Dayton, OH
Salary: $112,800–$257,000 / yr
Employment: Full-time
Posted: 20 days ago
Freshness: Confirmed live today
Closes: Dec 31, 2026
Nearby: 90 roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $204k

This role $185k

$95k $274k

below market most similar roles pay here above market

This role pays less than 72% of similar roles. Most pay $177,287–$231,025 — the blue band above. At the midpoint, this role pays about $185k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Booz Allen Hamilton

Booz Allen Hamilton is a management and technology consulting firm that provides analytics, digital, engineering, and cybersecurity solutions primarily to U.S. government agencies and commercial clients. Industry: Management & Technology Consulting

Booz Allen Hamilton currently has 1662 open roles on FindRole.

Listed pay typically runs $86,800–$198,000 across 904 roles with salary data.

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At a glance

TL;DR · Computer Vision Software Engineer, Lead

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The Computer Vision Software Engineer, Lead will join a collaborative technical delivery team to design, develop, and optimize advanced computer vision and multi-sensor fusion algorithms for GEOINT-mission workflows. The role involves leading the development of deep learning models, real-time tracking systems, and GPU-accelerated pipelines that integrate imaging physics and ML-based behavior inference. Key responsibilities include implementing technical solutions in Python or C++, utilizing GPU programming via CUDA or RAPIDS, and managing CI/CD pipelines using Kubernetes, Docker, or Jenkins. The position requires expertise in transformer-based or self-supervised architectures, classical tracking methods like Kalman filters, and Agile methodologies. This role solves complex problems in remote sensing by developing novel single and multi-sensor detection and fusion of targets within constrained computing environments.

Skills

Computer Vision Deep Learning Python C++ CUDA RAPIDS TensorRT Kubernetes Docker CI/CD Jenkins MLOps ONNX Runtime Triton Inference Server AWS GovCloud Azure ML Transformer Particle Filter multi‑sensor fusion

What you'll do

Develop and implement deep learning computer vision models for sensor fusion and target tracking.
Design and deploy technical solutions using Python or C++ in operational environments.
Optimize computer vision application performance using GPU programming, including CUDA or RAPIDS.
Architect and implement novel single and multi-sensor detection and tracking systems.
Design, test, and optimize algorithms for constrained computing environments like multi-GPU servers or cloud.
Manage model pipelines including training, evaluation, and deployment workflows.
Administer continuous integration and continuous deployment (CI/CD) pipelines using Kubernetes, Docker, or Jenkins.

What we're looking for

Bachelor’s degree in a STEM field.
Active TS/SCI clearance and willingness to take a polygraph exam.
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 CI/CD pipelines using Kubernetes, Docker, or Jenkins.
Experience with Agile methodology, extreme programming, software engineering, and product management.
Knowledge of GPU-accelerated development using CUDA, RAPIDS, or GPU programming frameworks.
Knowledge of classical tracking or estimation methods such as Kalman or extended filters.
Ability to design, test, and optimize algorithms for operational performance in constrained computing environments.
Experience with GPU-accelerated deep learning, including CUDA kernel development and TensorRT optimization (preferred).
Experience developing synthetic data, kinematic target models, or scenario simulation tools (preferred).
Experience with advanced estimation, tracking, and fusion techniques such as Bayesian fusion or particle filters (preferred).
Experience with MLOps or scalable deployment systems, including ONNX Runtime or Triton Inference Server (preferred).
Experience building or optimizing microservice architectures or distributed systems for real-time data processing (preferred).
Experience integrating CV models into edge, embedded, or latency-constrained operational environments (preferred).
Experience with transformer-based vision architectures beyond DINO, CLIP, or SAM (preferred).
Experience with cloud ML platforms such as AWS GovCloud or Azure ML (preferred).
Knowledge of geospatial data formats, sensor phenomenology, or remote-sensing exploitation workflows (preferred).
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 (preferred).