Jobs at Distributed Spectrum

Leads the embedded engineering team responsible for embedded Linux, hardware interfaces, on-device machine learning runtimes, OTA updates, fleet reliability, and deployed sensor software. Owns the embedded roadmap, technical architecture, delivery, hiring, and engineer development. Partners with ML, RF, hardware, and mission operations teams to build reliable software for constrained devices operating under memory, power, thermal, and connectivity limitations.

Lead frontend architecture and delivery for interfaces that help operators explore spectrum data, direct sensors, and collaborate with AI agents. Build high-performance visualizations, real-time streaming experiences, agentic workflows, component libraries, design systems, and testing practices. Partner with product, design, and backend teams to translate ambiguous requirements into polished, accessible, production-ready interfaces.

Own the platform infrastructure supporting inference services, data pipelines, agents, and UI applications. Design cloud infrastructure, CI/CD, infrastructure-as-code, deployment automation, autoscaling, security controls, observability, developer tooling, and incident response. The role supports AWS or other major cloud environments, Kubernetes workloads, GPU and CPU scaling, secure delivery pipelines, and reliable production operations.

Design and operate scalable backend services for spectrum data ingestion, ML inference, and autonomous agent workflows. Architect tool-calling, orchestration, state, memory, and evaluation systems; own model serving and performance optimization; define APIs and data contracts; and ensure production reliability through observability, alerting, and runbooks. The role also involves architectural leadership, design reviews, mentoring, and collaboration with ML researchers to productionize models.

Design and operate a large-scale AI/ML data platform spanning ingestion, lakehouse storage, cataloging, governance, vector search, dataset versioning, feature and embedding pipelines, observability, and low-latency retrieval. The role owns production reliability and performance, establishes data standards, supports reproducible model training, manages petabyte-scale storage, and mentors engineers while leading technical architecture.

Coordinates critical initiatives across engineering, research, product, and growth. Establishes success criteria, evaluates progress, manages dependencies, resolves coordination gaps, documents decisions, and ensures evidence informs milestones. The role works closely with founders and senior leaders, translating technical efforts into usable capabilities while maintaining alignment, accountability, and follow-through across the organization.

Operate and support live RF mission deployments, deploy and configure edge‑AI sensors, validate and collect data, troubleshoot systems, design mission workflows, train users, coordinate mission planning and debriefs, and collaborate with engineering to drive iterative improvements to hardware, software, and AI models.

Own travel logistics and operational coordination for the Growth team, including booking transportation and lodging, managing last-minute changes, organizing trip information, and keeping events moving. The role also supports customer and project information management, cross-company communication, process improvement, and light calendar, inbox, and administrative support for a senior leader.

Support field deployments, system setup, troubleshooting, and improve product workflow based on user feedback in defense technology.

As a Mission Operations Engineer, you will manage the deployment of AI-driven sensors for RF data collection, ensuring operational mission success by overseeing workflows, collaborating with engineering, and maintaining system performance.

Responsible for design, evaluation, and optimization of RF systems in complex electromagnetic environments, leading RF efforts on cross-functional teams.