Embedded Signal Processing Engineer

About the Role

The organization is seeking an Embedded Signal Processing Engineer to own the full lifecycle of deploying and optimizing production-ready AI systems for secure, distributed public sector environments. This position demands direct accountability for translating high-level algorithmic concepts into autonomous field deployments that operate reliably at the edge under tactical constraints. The hire will serve as the critical bridge between raw multisensor data and hardened real-time signal processing pipelines that function in restricted or disconnected settings. Success in this role requires driving the design, validation, and ruggedization of systems that maintain integrity when infrastructure is absent or intermittent. The individual will be responsible for ensuring that software-defined radio principles, physical layer optimizations, and cross-platform middleware work in concert to meet stringent operational demands. This role operates with a high degree of autonomy, requiring the candidate to proactively identify risks, propose robust solutions, and execute against strict timelines without direct supervision. The work directly supports national security and public sector missions by enabling AI capabilities in environments where reliability and data sovereignty are non-negotiable.

Key facts

What you'll do

Coordinate with AI and Network Software Engineers to design low-latency multi-node communication frameworks capable of operating in restricted and/or disconnected environments.

Quantize, compile, and deploy real-time multi-agent pipelines to ensure readiness across diverse edge architectures using x86 and Jetson/ARM64 platforms.

Develop algorithms for asynchronous multimodal data fusion, tracking, and signal processing that maintain fidelity under variable conditions.

Architect and maintain high-fidelity Hardware-in-the-Loop simulation environments to test autonomy algorithms rigorously before physical deployment.

Lead the physical assembly, system calibration, and ruggedized field-testing of multi-node prototypes in unstructured terrain and harsh operational settings.

Perform detailed power, size, weight, and cost analysis to meet strict SWaP-C requirements while preserving real-time performance and reliability.

Implement multi-stream ingestion strategies and network partitioning logic that optimize bandwidth usage in environments with intermittent connectivity.

Integrate custom hardware interfaces and drivers to connect sensors and communication modules within larger distributed systems.

Validate signal processing chains through empirical bench testing, ensuring that theoretical models align with real-world performance metrics.

Travel occasionally to other webAI office locations and to government field and test sites across the country to support operational needs and on-site calibration.

Conduct code reviews, documentation, and knowledge transfer sessions to ensure continuity and maintainability of deployed systems.

Troubleshoot emergent issues in deployed systems, applying root cause analysis to refine algorithms and improve resilience over time.

Champion best practices for security, privacy, and compliance in all hardware and software deliverables related to government work.

Mentor junior engineers by providing guidance on embedded development, signal processing techniques, and deployment methodologies.

What You Will Learn

webAI acknowledges that it is rare to possess AI experience alongside the full scope of responsibilities listed above. The company is committed to training the successful hire in AI fundamentals. Training will cover various aspects encountered in the role, including computer vision and edge AI systems, to ensure proficiency in the position's technical demands. You will learn how to adapt models to constrained hardware, optimize inference latency, and validate performance in field conditions. The training program is designed to bring engineers up to speed on modern toolchains for quantization, compilation, and deployment across heterogeneous compute platforms. You will also gain exposure to secure data handling practices, compliance requirements, and the operational realities of running AI at the edge for public sector clients. This role provides a foundation in both the theoretical and practical aspects of embedded AI, preparing you for long-term growth in distributed infrastructure.

Requirements

Bring a minimum of 6 years of professional experience in RF and networking architecture, tactical radio systems, physical bench-test simulation environments, and distributed middleware.

Demonstrate hands-on experience with embedded systems, signal processing, and the integration of custom hardware into larger systems that meet defense-oriented standards.

Show a strong understanding of physical SWaP-C (Size, Weight, Power, Cost) optimization, network partitioning, multi-stream ingestion, and cross-platform development across systems such as x86 and Jetson/ARM64.

Prove expertise in C/C++ programming and working within Linux environments, including real-time operating systems and device driver integration.

Hold a Bachelor's degree in a relevant field, or possess equivalent practical experience, including military service, which is valued for its applied rigor and operational discipline.

Candidates with advanced field experience are heavily preferred and should demonstrate a track record of deploying technology in challenging, real-world conditions.

You must be eligible for U.S. citizenship and capable of obtaining a secret security clearance, as this role involves access to sensitive government information.

You must be physically capable of participating in field testing and travel to remote or rugged environments as required by mission objectives.

Nice to have

Have direct experience developing, testing, or deploying autonomous software on tactical military or defense systems that adhere to open architecture standards.

Have a proven history of conducting field validation, site calibration, or operational testing for edge networks and ruggedized hardware within civilian commercial or tactical defense environments.

Practical notes

This role requires the ability to work flexible hours. Travel and field testing are mandatory components of the position. U.S. citizenship and a secret security clearance are mandatory for this role. Privacy and compliance practices apply to all government work, ensuring that data handling meets all contractual standards.