Computer Vision Software Engineer, Lead

Job Number: R0245705 Computer Vision Software Engineer, Lead The Opportunity: As a Senior Computer Vision Engineer, you will design, develop, and optimize advanced computer vision and multisensor fusion algorithms supporting GEOINTmission workflows. You will lead the development of deep learning models, realtime tracking systems, and GPUaccelerated pipelines deployed in operational environments. You will shape nextgeneration tools that integrate imaging physics, ML-based behavior inference, and multisensor fusion. You'll join a collaborative technical delivery team where you'll contribute to secure, reliable, and usercentric tools that accelerate mission outcomes. What You'll Work On: - 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. Join us. The world can't wait. You Have: - 6+ years of experience developing computer vision algorithms for detection, tracking, or multisensor fusion in remote sensing or GEOINT environments - 2+ years of experience applying deep learning to computer vision problems using transformerbased or selfsupervised architectures - Experience implementing model pipelines in Python or C++, including training, evaluation, and deployment workflows - Experience with administration of continuous integration or continuous deployment (CI/CD) pipelines using Kubernetes, Docker, or Jenkins - Experience with Agile methodology, extreme programming, software engineering, product management, and software products, and acquiring client requirements and resolving workflow problems through automation optimization - Knowledge of GPUaccelerated development using CUDA, RAPIDS, or GPU programming frameworks - Knowledge of classical tracking or estimation methods such as Kalman or extended filters, to support realtime algorithm development - Ability to design, test, and optimize algorithms for operational performance in constrained computing environments such as multiGPU servers or cloud - Active TS/SCI clearance; willingness to take a polygraph exam - Bachelor's degree in a STEM field Nice If You Have: Experience with GPUaccelerated deep learning, including CUDA kernel development, TensorRT optimization, RAPIDS, or distributed multiGPU 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 multisensor registration, Bayesian fusion, particle filters, or deep multiobject tracking pipelines Experience with MLOps or scalable deployment systems, including Docker, Kubernetes, ONNX Runtime, or Triton Inference Server Experience building or optimizing microservice architectures or distributed systems for realtime data processing Experience integrating CV models into edge, embedded, or latencyconstrained operational environments Experience with transformerbased vision architectures beyond DINO, CLIP, or SAM such as ViT variants or selfsupervised multimodal encoders Experience with cloud ML platforms such as AWS GovCloud, Azure ML, or onpremises GPU clusters Knowledge of geospatial data formats, sensor phenomenology, or remotesensing exploit