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 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.
Compensation
At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.
Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $112,800.00 to $257,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen’s total compensation package for employees. This posting will close within 90 days from the Posting Date.
Identity Statement
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.
Candidate AI Usage Policy
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. 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 or customer facility, in alignment with your leadership's expectations and the needs of the role.
Onsite: If this position is listed as onsite, work will primarily be performed full-time at a customer facility, where employees will collaborate directly with colleagues and customers as required by the role.
Commitment to Non-Discrimination
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.
