Senior Computer Vision Engineer

About the role

CFD Research has delivered innovative technology solutions within the Aerospace & Defense, Biomedical & Life Sciences, Intelligence & Sensing, and Energy & Materials industries since 1987. The company has earned multiple national awards for successful application and commercialization of innovative component/system technology prototypes, multi-physics simulation software, multi-disciplinary analyses, and expert support services. Headquartered in Huntsville, Alabama with laboratory facilities there, CFD Research also has offices and labs in Dayton, Ohio; prototyping and test facilities in Hollywood, Alabama; and office facilities in Fort Walton Beach, Florida. It is an ISO9001:AS9100D registered company, appraised at CMMI Level II for Services, and a 100% ESOP (employee-owned) recognized in Inc. Magazine's Inc5000 as a top growing company for four of the last five years. CFD Research is seeking a Senior Computer Vision Engineer to design, develop, and optimize advanced computer vision and multi‑sensor fusion algorithms supporting GEOINT‑mission workflows. The candidate will participate in developing deep learning models, real‑time tracking systems, and GPU‑accelerated pipelines deployed in operational environments, shaping next‑generation tools that integrate imaging physics, ML‑based behavior inference, and multi‑sensor fusion. CFD Research values first‑principles reasoning and physically interpretable models, so experience developing hybrid systems that combine calibrated sensor models, deterministic algorithms, probabilistic inference, and learned components is particularly desirable. Candidates should be motivated by building systems that are scientifically explainable, empirically validated, and capable of operating robustly across both synthetic and real‑world sensing environments.

Location: Dayton, OH area, 100% onsite.

Responsibilities

• 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

Qualifications

Basic Qualifications

• 6+ years of experience developing computer vision algorithms for detection, tracking, or multi‑sensor fusion in remote sensing or GEOINT‑relevant environments
• 2+ years of experience applying deep learning to computer vision problems using transformer‑based or self‑supervised architectures
• Candidate must possess an ACTIVE TS/SCI security clearance to be considered.
• Experience implementing model pipelines in Python or C++, including training, evaluation, and deployment workflows
• Knowledge of GPU‑accelerated development using CUDA, RAPIDS, or other GPU programming frameworks
• Knowledge of classical tracking or estimation methods (such as Kalman or extended filters) to support real‑time algorithm development
• Knowledge of Physics-Informed Neural Networks (PINNs), Physics-constrained loss functions
• Ability to design, test, and optimize algorithms for operational performance in constrained computing environments (i.e., multi‑GPU servers or cloud)
• Bachelor’s degree in related STEM field
• Candidate must be a U.S. Citizen

Desired Qualifications

• 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, deep multi‑object tracking pipelines, etc.)
• Knowledge of geospatial data formats, sensor phenomenology, or remote‑sensing exploitation workflows
• Experience with MLOps or scalable deployment systems (Docker, Kubernetes, ON