Computer Vision Software Engineer, Lead

Computer Vision Software Engineer, Lead

The Opportunity:
As a Senior Computer Vision Engineer, you will design, develop, and optimize advanced computer vision and multi‑sensor fusion algorithms supporting GEOINT‑mission workflows. You will lead 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.

You’ll join a collaborative technical delivery team where you’ll contribute to secure, reliable, and user‑centric 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 multi‑sensor fusion in remote sensing or GEOINT 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

• 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 GPU‑accelerated development using CUDA, RAPIDS, or GPU programming frameworks

• Knowledge of classical tracking or estimation methods such as Kalman or extended filters, to support real‑time algorithm development

• Ability to design, test, and optimize algorithms for operational performance in constrained computing environments such as multi‑GPU 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 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, or deep multi‑object 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 real‑time data processing

• Experience integrating CV models into edge, embedded, or latency‑constrained operational environments

• Experience with transformer‑based vision architectures beyond DINO, CLIP, or SAM such as ViT variants or self‑supervised multi‑modal encoders

• Experience with cloud ML platforms such as AWS Gov Cloud, Azure ML, or on‑premises GPU clusters

• Knowledge of geospatial data formats, sensor phenomenology, or remote‑sensing exploitation workflows

• Master’s degree in CS, Electrical Engineering, Computer Engineering, AI/ML, Physics, or Mathematics preferred;
  Doctorate degree in CS, Electrical Engineering, Computer Engineering, AI/ML, Physics, or Mathematics a plus

Clearance:
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; TS/SCI clearance is required.

Compensation

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