Senior AI Software Engineer

Expression•Washington, DC

1d•$180,000 - $210,000•Hybrid

About The Position

Expression is seeking an experienced Senior AI Software Engineer and Technical Lead to lead the development of a prototype platform that performs intelligent data processing, distributed inference, and resilient edge computing in communication-constrained environments. The platform integrates software-defined radio (SDR) technologies, edge AI, distributed systems, and modern software engineering practices to enable autonomous processing and decision support at the tactical edge. The ideal candidate is an experienced hands-on engineer who enjoys building complex systems from the ground up. You are equally comfortable designing system architecture, writing production-quality code, integrating hardware and software components, and mentoring other engineers while remaining deeply involved in implementation. Security Clearance: Eligible to obtain Secret or Top Secret Clearance (U.S citizenship required)

Requirements

Bachelor degree in Computer Science, Software Engineering, Electrical Engineering, Data Science, or related fields. An advanced degree is preferred.
10+ years of professional software engineering experience designing and building distributed software systems.
Demonstrated experience serving as technical lead of complex software development efforts.
Strong Python experience including, asyncio and asynchronous programming, FastAPI, Pydantic, type hinting, and modern development practices.
Experience developing distributed systems, microservices, or event-driven architectures.
Proven experience implementing retrieval architectures.
Experience deploying applications using Docker and cloud-native services within AWS or Azure.
Experience implementing CI/CD pipelines using GitLab CI, GitHub Actions, or similar automation platforms.
Experience with Kubernetes, Infrastructure as Code (Terraform and Helm, or equivalent), and modern cloud-native deployment practices.
Experience integrating hardware and software systems.
Experience designing resilient software systems capable of operating under degraded or intermittent network conditions.
Strong written and verbal communication skills with the ability to produce technical documentation and communicate effectively with technical and executive stakeholders.

Nice To Haves

Experience with Software Defined Radio (GNU Radio, USRP, Ettus, SoapySDR, UHD, etc.)
Experience developing edge computing applications.
Experience with embedded Linux.
Experience supporting DoD, Intelligence Community, or Federal programs.
Experience with distributed data synchronization technologies.
Experience with low-latency data processing pipelines.
Experience working with autonomous systems, ISR, electronic warfare, or sensor fusion applications.
Experience integrating AI/ML inference into operational software systems.
Experience supporting National Security or Federal Civilian customers.

Responsibilities

Lead the technical architecture and implementation of an edge-native intelligent processing platform supporting distributed sensing, edge inference, and resilient mission operations.
Design scalable software architectures supporting heterogeneous edge computing devices, software-defined radios, embedded compute platforms, and cloud-enabled mission services.
Develop high-performance backend services using Python, FastAPI, asynchronous programming, and modern distributed software engineering techniques.
Lead integration of Software Defined Radio (SDR) platforms (such as Ettus USRP, GNU Radio, or equivalent SDR ecosystems) into software pipelines for real-time data processing and analytics.
Design resilient distributed processing frameworks capable of operating under degraded, disconnected, intermittent, and bandwidth-constrained communications.
Develop intelligent data processing pipelines that reduce communication overhead through feature extraction, prioritization, and adaptive data management.
Design peer-to-peer communication services supporting decentralized collaboration across heterogeneous edge devices.
Implement secure synchronization, workload distribution, and fault-tolerant processing across multiple edge computing nodes.
Lead software integration across embedded Linux systems, containerized services, edge compute devices, and cloud infrastructure.
Establish software architecture standards, CI/CD pipelines, testing frameworks, DevSecOps practices, and engineering best practices.
Collaborate closely with AI engineers to integrate machine learning inference capabilities into production software.
Produce technical documentation, software architecture artifacts, interface specifications, and engineering design documentation.
Support technical demonstrations, customer engagements, and prototype evaluations.
Mentor junior engineers while maintaining significant hands-on software development responsibilities.