Jobs at Expedition Technology, Inc.

Develop and optimize ML and computer vision models for hyperspectral and multi-modal sensor data, build detection/tracking and data fusion algorithms, translate research into deployable software, and support solutions across cloud and low SWaP environments for geospatial intelligence applications.

Design and implement DSP solutions for intelligence and defense applications, researching advanced DSP and machine learning algorithms for radar, electronic warfare, SIGINT, and RF geolocation. Develop models and transition them into real-time, field-ready software. Responsibilities include prototyping, full-system design, deployment across on-premises and cloud platforms, and operational integration.

Deploy and optimize computer vision, signal processing, and machine learning algorithms on GPU-enabled embedded systems. Improve real-time performance on constrained low-SWaP platforms, profile CPU/GPU bottlenecks, select algorithms based on hardware constraints, containerize deployments, and produce Linux-based production software. Collaborate with machine learning and software engineers to transition models into operational environments.

Design and implement backend and systems-level software for defense and intelligence applications. Integrate machine learning and signal-processing algorithms into real-time systems, transition models into field-ready applications, collaborate with domain experts, and build extensible software solutions for complex customer requirements.

Design and implement complex backend and systems-level software for defense and intelligence applications. Integrate machine learning and signal-processing algorithms, transition models into real-time field systems, architect and integrate software systems, prototype algorithms, and translate customer requirements into extensible solutions.

Designs and leads scalable Python software systems supporting U.S. Government missions. Responsibilities include architecture, distributed systems, cloud-native Kubernetes deployments, CI/CD automation, observability, reliability, troubleshooting, testing, and operational excellence. The role provides technical leadership and mentorship while collaborating with engineers and mission stakeholders. Candidates must hold an active TS/SCI clearance and bring extensive production software engineering experience, cloud expertise, and strong system integration skills.

Design and implement production-grade Python services and internal tools supporting verification, validation, testing, and evaluation workflows for DoD and intelligence missions. Build scalable, maintainable, and testable systems; establish engineering, testing, and observability best practices; contribute to mission-critical architecture; support containerized deployments and CI/CD automation; and mentor teammates.

Develop and deploy DevSecOps capabilities for defense-focused prototype systems. Responsibilities include containerizing Python applications, deploying workloads to Kubernetes, building CI pipelines and developer tooling, delivering infrastructure as code, troubleshooting Linux networking and storage, supporting cloud-native applications, and ensuring compliance with NIST, CMMC, and CIS controls.

Develop deep learning architectures and end-to-end machine learning pipelines for intelligence and national security applications. Responsibilities include sourcing and curating data, training, evaluating, testing, and deploying computer vision models in AWS using Python and PyTorch. The role also involves producing high-quality software, researching academic literature, collaborating on innovative solutions, and presenting technical work.

Develop and operate secure, cloud-native DevSecOps capabilities, including CI/CD pipelines, container orchestration, infrastructure as code, automation, and release engineering. Write and review Python code, troubleshoot Linux networking and storage, and support compliance with NIST, CMMC, CIS, and related standards. Partner with software engineers across defense and intelligence programs to deliver scalable operational capabilities.

Designs GitLab CI/CD pipelines, optimizes Kubernetes deployments, develops production-grade Python tools and applications, and supports mission-critical DoD and intelligence systems. The role emphasizes containerization, observability, testing, resilient system design, and collaboration across engineering teams. Responsibilities also include mentoring teammates and contributing to verification, validation, and evaluation environments.

Develop backend features, automated workflows, APIs, streaming services, and machine learning pipelines using Python, Docker, Kubernetes, and AWS. Collaborate on deep learning and computer vision applications, improve system performance and reliability, resolve bottlenecks, and follow software development and coding best practices in an Agile environment. The role requires an active Top Secret/SCI clearance and supports defense and intelligence missions.

Build and maintain full-stack applications for a mission-critical DoD and intelligence community training data platform. Responsibilities include developing responsive frontends, Python backend services, REST APIs, cloud-native systems, and containerized deployments on AWS. The role requires collaboration in Agile teams, testing, code reviews, performance and security optimization, user engagement, and support for scalable AI/ML data and model operations.

Design, develop, deploy, and maintain scalable, resilient, containerized cloud-native applications and infrastructure on AWS GovCloud and C2S. Manage cleared cloud deployments, develop reusable CloudFormation templates, implement logic with Lambda and ECS Fargate, support CI/CD and DevOps practices, and ensure compatibility across cleared and uncleared environments.

Develop deep learning architectures and end-to-end computer vision systems for national security applications. Responsibilities include researching technical literature, curating data, building and evaluating models, implementing solutions in Python and PyTorch, deploying systems in AWS, testing software, and collaborating on innovative solutions. The role requires strong machine learning theory, software engineering, object-oriented design, cloud development, CI/CD, containerization, and software lifecycle experience.

Develop and maintain backend capabilities for a DoD and Intelligence Community AI/ML training data repository. Responsibilities include building Python features, automated workflows, RESTful APIs, streaming services, and cloud-native systems using Docker, Kubernetes, and AWS. The role involves database design and optimization, performance troubleshooting, Agile collaboration, technical leadership, mentoring, and direct engagement with end users. An active TS/SCI clearance and at least five years of backend development experience are required.

Develop and deploy scalable, resilient, containerized cloud-native applications on AWS. Manage cleared C2S and GovCloud infrastructure, maintain DevOps practices, support CI/CD, create reusable CloudFormation templates, and develop compatible infrastructure across cleared and uncleared environments. The role requires an active Top Secret/SCI clearance and U.S. citizenship, with Kubernetes and backend software engineering experience preferred.

Develop and maintain responsive front-end web applications for a defense and intelligence training data platform. Translate UI/UX designs into user-facing components, integrate interfaces with backend services through RESTful APIs or GraphQL, optimize performance, implement secure communications, manage code with Git, and create unit, integration, and end-to-end tests. Collaborate with designers, engineers, Agile teams, and end users throughout the software development lifecycle.

Develop scalable AI-enabled software integrating machine learning models, APIs, data pipelines, retrieval systems, and agentic workflows. Build backend services and cloud-native applications using Python, AWS, Docker, and Kubernetes. Operationalize machine learning capabilities, improve system performance and reliability, and support model inference and information retrieval in mission environments. Collaborate with engineers and researchers while following software development, testing, and quality practices.

Summer intern developing digital signal processing and machine learning solutions for real-time RF data. Responsibilities include creating Python-based filtering, spectral analysis, waveform generation, GPU-accelerated algorithms, data pipelines, ML training systems, and MLOps deployments. The role is onsite in Herndon, Virginia, full-time for Summer 2027, and requires U.S. citizenship and willingness to undergo a government security-clearance investigation.