Job 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 www.cfd-research.com CFD Research is looking for a Senior Computer Vision Engineer who will design, 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: 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, 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 (such as: ViT variants, self‑supervised multi‑modal encoders) Experience with cloud ML platforms (such as: 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 other related field Location: This role is based in the Dayton, OH area , and is 100% onsite. Benefits: CFD Research offers competitive salaries and excellent employee benefits, including an employer matching 401(k) and Employee Stock Ownership Plan (ESOP). CFD Research offers our Full-Time, permanent employee-owners a highly competitive insurance package, including medical, vision, and dental insurance. Additionally, we offer company paid leave, compensation time, parental leave, long-term and short-term disability, accidental death and dismemberment, and life insurance. Part-Time and Temporary employee-owners may receive partial or reduced benefits and leave time based on their employment status level. Performance appraisals occur twice a year and annual pay increases are based upon corporate goals, personal development, performance, and outstanding achievements. In addition, group and individual bonuses are awarded for exceptional performance. CFD Research is an EO employer - Veterans/Disabled and other protected categories

Requirements

Must-haves

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
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)

Nice-to-haves

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, 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 (such as: ViT variants, self‑supervised multi‑modal encoders)
Experience with cloud ML platforms (such as: 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 other related field
Location: This role is based in the Dayton, OH area , and is 100% onsite.
CFD Research is an EO employer - Veterans/Disabled and other protected categories

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Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave