Computer Vision Engineer - Real-Time Sensor Fusion

deciBel research; Beavercreek (OH); On-site; USD 110,000 - 170,000; Full time

Job summary

deciBel Research, Inc in Dayton, OH is seeking a Computer Vision Engineer to design, develop, and optimize advanced CV and multi-sensor fusion algorithms supporting GEOINT workflows. You will lead the development of deep learning models, real-time tracking systems, and GPU-accelerated pipelines deployed in operational environments.

Collaborate with multidisciplinary teams in Python or C++, leverage CUDA/RAPIDS for performance, and contribute to robust sensing solutions that integrate imaging

Qualifications

• BS in Computer Science, Electrical/Computer Engineering, AI/ML, Physics, Mathematics or another related field
• 3+ 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
• 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)
• 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 architecture 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 another related field

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

Skills

Deep learning

Computer vision

Sensor fusion

Python

C++

GPU programming

CUDA

RAPIDS

Real-time systems

Education

Bachelor's degree in Computer Science, Electrical/Computer Engineering, AI/ML, Physics, Mathematics

Master's degree or Ph.D. in Computer Science, Electrical/Computer Engineering, AI/ML, Physics

Tools

CUDA

RAPIDS

TensorRT

ONNX Runtime

Kubernetes

Job description

deciBel Research, Inc in Dayton, OH is seeking a Computer Vision Engineer to design, develop, and optimize advanced CV and multi-sensor fusion algorithms supporting GEOINT workflows. You will lead the development of deep learning models, real-time tracking systems, and GPU-accelerated pipelines deployed in operational environments.

Collaborate with multidisciplinary teams in Python or C++, leverage CUDA/RAPIDS for performance, and contribute to robust sensing solutions that integrate imaging