Computer Vision Engineer (TS/SCI)
Develops and optimizes computer vision and multi-sensor fusion algorithms for GEOINT-mission workflows, building deep learning detection/tracking models and GPU-accelerated pipelines deployed in operational environments. Core stack: Python/C++, CUDA/RAPIDS, transformer-based deep learning, and physics-informed ML, onsite in Dayton, OH.
- Compensation
- Not disclosed
- Location
- Dayton
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Part time
- Experience
- 1+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 1-5+ years of experience developing computer vision algorithms for detection, tracking, or multi-sensor fusion in remote sensing or GEOINT-relevant environments
- 1+ years of experience applying deep learning to computer vision problems using transformer-based or self-supervised architectures
- Active TS/SCI security clearance
- U.S. citizenship
- 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 (e.g., Kalman or extended filters)
- Knowledge of Physics-Informed Neural Networks (PINNs) and physics-constrained loss functions
- Ability to design, test, and optimize algorithms for operational performance in constrained computing environments (multi-GPU servers or cloud)
- Bachelor's degree in a related STEM field
Nice to have
- Experience with GPU-accelerated deep learning: CUDA kernel development, TensorRT optimization, RAPIDS, or distributed multi-GPU training
- Experience developing synthetic data, kinematic target models, or scenario simulation tools for algorithm training or evaluation
- Experience with advanced estimation, tracking, and fusion techniques (multi-sensor registration, Bayesian fusion, particle filters, deep multi-object tracking)
- Knowledge of geospatial data formats, sensor phenomenology, or remote-sensing exploitation workflows
- Experience with MLOps or scalable deployment systems (Docker, Kubernetes, ONNX Runtime, Triton Inference Server, or similar)
- Experience building or optimizing microservice architectures or distributed systems for real-time data processing
- Experience integrating CV models into edge, embedded, or latency-constrained operational environments
- Familiarity with transformer-based vision architectures beyond DINO/CLIP/SAM (ViT variants, self-supervised multi-modal encoders)
- Experience with cloud ML platforms (AWS GovCloud, Azure ML, on-prem GPU clusters)
- Master's degree or Ph.D. in Computer Science, Electrical/Computer Engineering, AI/ML, Physics, Mathematics, or related field
Full description
About CFD Research: Since its inception in 1987, CFD Research has delivered innovative technology solutions within the Aerospace & Defense, Biomedical & Life Sciences, Intelligence & Sensing, and Energy & Materials industries. CFD Research 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. Based in Huntsville, Alabama where laboratory facilities and headquarters are located, CFD Research also has office and laboratory facilities in Dayton, Ohio, prototyping test and evaluation facilities in Hollywood, Alabama, and office facilities in Fort Walton Beach, Florida. CFD Research is an ISO9001:AS9100D registered company and is appraised at CMMI Level II for Services. CFD Research is a 100% ESOP (employee-owned company) recognized in Inc. Magazine's Inc5000 as a top growing company for four of the last five years. Learn more at
CFD Research is looking for a Computer Vision Engineer who will develop, and optimize advanced computer vision and multi‑sensor fusion algorithms supporting GEOINT‑mission workflows. The candidate will participate in the development of deep learning models, real‑time tracking systems, and GPU‑accelerated pipelines deployed in operational environments. You will shape 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.
- 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
Basic Qualifications:
- 1-5+ years of experience developing computer vision algorithms for detection, tracking, or multi‑sensor fusion in remote sensing or GEOINT‑relevant environments
- 1+ 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.