Top Tech Jobs & Startup Jobs

Design, prototype, and operationalize ML models and data pipelines for temporal, geospatial, and track-based mission data. Build data processing, feature engineering, containerized cloud deployments (AWS/Docker/Kubernetes), APIs, and scalable evaluation/monitoring workflows while following Agile and software engineering best practices.

Design, develop, and maintain full-stack web applications and scalable backend services for a DoD/IC Training Data Storefront. Implement responsive frontends, Python-based APIs, containerized deployments on AWS, and ensure performance, security, and test coverage while collaborating in an Agile team.

Design and implement advanced digital signal processing and machine learning algorithms for radar, EW, SIGINT, and RF geolocation; prototype and transition DSP/ML models to real-time, field-ready software across on‑premises and cloud environments in support of US intelligence and defense missions.

Develop and deploy deep learning computer-vision systems: source and curate data, design and train models (object detection), implement production-ready PyTorch/Python software, integrate with AWS, apply CI/CD and testing, and collaborate on research and technical solutions for intelligence-focused applications.

Design, develop, and deploy cloud-native applications on AWS for cleared environments. Manage cleared cloud deployments (C2S/GovCloud), build reusable CloudFormation templates, support DevOps/CI-CD pipelines, and containerized applications.

Build and maintain Python-based services and internal tools to support verification, validation, testing, and evaluation workflows for DoD and Intel missions. Design scalable, testable software systems, collaborate on architecture and observability, support containerized deployments and CI/CD automation, and mentor engineering teammates in mission-critical environments.

Develop backend systems and ML pipelines for computer-vision applications using Python, Docker, Kubernetes and AWS. Build RESTful APIs and streaming services, optimize performance, follow SDLC best practices, and collaborate in an Agile team to deploy containerized deep learning models.