Computer Vision Engineer
Computer Vision Engineer
Posted 43 days ago
SENIORITY
Lead
About the role
- 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. 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 fieldU.S. Citizenship is REQUIRED.
- 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
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