Lead Applied AI Engineer

STR•Woburn, MA

2h•$180,000 - $230,000

About The Position

STR’s Intelligence Division researches, develops, and transitions advanced analytics and machine-learning solutions for national-security problems. The team applies expertise in engineering, computer science, mathematics, and data science to mission-critical work across networking, communications, social media, text, time-series, geospatial, and imagery data. Its tools and technologies help U.S. Government analysts extract, enrich, and act on intelligence information worldwide. The Lead Applied AI Engineer leads technical development and delivery of RF AI/ML tools, algorithms, and software integrated with physical hardware, sensors, and compute. The role focuses on RF communications, Open RAN, 5G/6G communications, waveform generation and adaptation, and wireless communications and networking infrastructure. It leads design, implementation, integration, testing, demonstration, and transition of mission-relevant software spanning data processing, AI/ML models, communications services, and edge or on-premises compute environments. The engineer owns technical delivery for an assigned project or major subsystem, translating mission needs into executable plans and guiding architecture, implementation, integration, verification, and transition. The role works with software engineers, systems engineers, RF/communications researchers, DevSecOps personnel, and mission partners to develop scalable, secure, maintainable capabilities for complex customer environments. It defines and decomposes technical work, leads assigned engineers and contributors, manages technical interfaces and risks, and communicates progress, decisions, and tradeoffs to the Principal Investigator, project team, and mission partners.

Requirements

PhD with at least 2 years, MS with at least 6 years, or BS with at least 8 years of relevant experience in computer science, software engineering, computer engineering, or a related technical discipline.
Ability to obtain and maintain a Top Secret security clearance; U.S. citizenship is required by the U.S. Government for this eligibility.
Experience leading software development efforts, major workstreams, or complex subsystems; ability to define tasks and subtasks, coordinate contributors, manage technical interfaces and risks, and communicate progress and tradeoffs to stakeholders.
Experience translating customer needs into requirements, designs, development and test plans, and integrated capabilities.
Experience managing interfaces among software, AI/ML, RF, communications, hardware, data, and deployment components.
Experience applying AI/ML to operational problems involving sensor, time-series, RF, communications, or other non-textual data.
Proficiency in Python, C++, Java, Go, or a similar programming language, with experience developing and maintaining production-quality software.
Experience with Git, code review, automated testing, CI/CD, containers, issue tracking, documentation, and secure software-development practices.
Ability to lead technical discussions, communicate risks and tradeoffs, and work effectively with customers and multidisciplinary teams.

Nice To Haves

Experience with RF signal processing, wireless communications, 5G/6G RAN, O-RAN/AI-RAN, waveform generation/adaptation, or edge AI.
Active Secret, Top Secret, or TS/SCI security clearance.
Experience designing system or software architectures for distributed, data-intensive, real-time, cloud, on-premises, edge, RF sensing, or communications systems.
Experience with digital signal processing, software-defined radios, PHY/MAC processing, or RF sensing.
Familiarity with 5G/6G or Open RAN architectures and deployment of optimized models to edge AI/ML accelerators.
Experience developing high-throughput, latency-sensitive software for PHY, MAC, scheduling, or radio-resource management.
Experience with CI/CD, infrastructure automation, container orchestration, cloud platforms, monitoring, logging, and security tooling.
Experience developing software in large, multi-team codebases using collaborative engineering practices.
Familiarity with message queues, stream processing, distributed computing, or microservices.
Experience supporting technical proposals, customer demonstrations, technical briefings, or capture activities with DoW or IC customers.
Experience using LLM-enabled development tools, such as Cursor or Codex, in a secure and effective engineering workflow.

Responsibilities

Lead development and delivery of AI/ML, RF sensing, and communications/networking capabilities from requirements definition through implementation, integration, testing, demonstration, deployment, and sustainment.
Translate 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 needs for performance, latency, reliability, spectrum efficiency, security, and deployability.
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.
Decompose complex work into actionable tasks and subtasks; manage dependencies, technical risks, interfaces, and progress toward program objectives.
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 applicable RF sensing and communications capabilities, including spectrum sensing, signal classification, interference mitigation, channel estimation, beam management, adaptive links, and passive-radar processing.
Lead design reviews, code reviews, integration, testing, customer demonstrations, and release readiness.
Ensure systems meet reliability, security, maintainability, and performance requirements, including applicable high-throughput, low-latency edge AI workflows such as Layer 1/Layer 2 functions.
Contribute technical content to proposals, customer engagements, and transition planning, and ensure delivered capabilities are reusable and mission relevant.