Computer Vision Software Engineer, Lead

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 met hodology, extreme programming, sof tware engineering, product management, and sof tware 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 met hods 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

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 GovCloud, 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.

As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.

AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided .

Work Model Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.

Remote : If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.

Hybrid : If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.

Onsite : If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.

All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.