About the Team

STR’s Intelligence Division researches, develops, and transitions advanced analytics and machine-learning solutions for challenging national-security problems. Our team includes engineers and researchers with advanced backgrounds in engineering, computer science, mathematics, and data science who apply deep technical expertise and creativity to mission-critical challenges.

Our work spans data analysis, deep learning, and statistical methods across networking, communications, social media, text, time series, geospatial, and imagery data. We develop capabilities under constraints not typically encountered in commercial environments. U.S. Government analysts use our tools and technologies to extract, enrich, and act on intelligence information worldwide.

The Role

As a Lead Applied AI Engineer, you will lead the technical development and delivery of RF AI/ML-based tools, algorithms, and software integrated with physical hardware, sensors, and compute, focusing on Radio Frequency (RF) communications, Open RAN, 5G/6G communications, waveform generation/adaptation, and wireless communications and networking infrastructure. You will lead the design, implementation, integration, test, demonstration, and transition of mission-relevant software spanning data processing, AI/ML models, communications services, and edge or on-premises compute environments.

You will own technical delivery for an assigned project or major subsystem, translating mission needs into executable plans and guiding architecture, implementation, integration, verification, and transition. You will work closely with software engineers, systems engineers, RF/communications researchers, DevSecOps personnel, and mission partners to develop scalable, secure, maintainable capabilities for complex customer environments.

This is a technical leadership role. You will define and decompose technical work, lead assigned engineers and contributors, manage technical interfaces and risks, and communicate progress, decisions, and tradeoffs to the Principal Investigator, project team, and mission partners.

What You Will Do

Lead project execution: Lead development and delivery of AI/ML, RF sensing, and communications/networking capabilities from requirements definition through implementation, integration, test, demonstration, deployment, and sustainment
Translate mission needs into plans: Convert customer objectives into requirements, architectures, development plans, milestones, performance measures, and acceptance criteria. Architect and evaluate AI/ML-enabled RF, communications, and networking solutions, including 5G/6G AI-RAN capabilities, against mission performance, latency, reliability, spectrum efficiency, security, and deployability needs.
Lead the technical team: Lead and mentor engineers and researchers; collaborate with systems engineers, RF/communications researchers, DevSecOps personnel and mission partners; and communicate technical status, decisions, risks, and tradeoffs
Manage technical work: Decompose complex work into actionable tasks and subtasks; manage dependencies, technical risks, interfaces, and progress toward program objectives
Own project architecture: Design and oversee architectures spanning RF and sensor-data pipelines, AI/ML integrations, services, APIs, databases, user interfaces, Open RAN integrations, and deployment infrastructure. Lead development of RF sensing and communications capabilities such as spectrum sensing, signal classification, interference mitigation, channel estimation, beam management, adaptive links, and passive-radar processing, as applicable to project needs
Drive integration and quality: Lead design reviews, code reviews, integration, testing, customer demonstrations, and release readiness. Ensure systems meet reliability, security, maintainability, and performance requirements, including high-throughput, low-latency edge AI workflows (e.g., Layer 1/Layer 2 functions) where applicable
Support growth and transition: Contribute technical content to proposals, customer engagements, and transition planning while ensuring delivered capabilities are reusable and mission relevant