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Distributed Spectrum builds systems for radio spectrum intelligence, combining signal processing, neural networks, and embedded systems to solve a technically novel problem. The company has developed a custom stack including proprietary embedding model architectures, custom GPU kernels, and specialized infrastructure.
As Senior Backend Engineer, you will design and build core platform services that ingest spectrum data, orchestrate ML inference, and power autonomous agents. This is a high-ownership role where you will set technical direction, make multi-year architecture decisions, and mentor other engineers.
Key responsibilities include:
- Design, build, and operate reliable, observable backend services that scale with the sensor fleet and customer base
- Architect the agent layer: tool-calling interfaces, orchestration, state management, memory, and evaluation harnesses for LLM-driven agents
- Own the ML inference architecture including model serving, batching, GPU/CPU scheduling, latency and cost optimization, and inference APIs
- Define service boundaries, data contracts, and API standards across the backend
- Build production-grade systems with instrumentation, alerting, graceful degradation, and clear runbooks
- Partner with ML researchers to move models from experiment to production quickly and safely
- Lead design reviews, mentor engineers, and raise engineering standards
The role sits at the intersection of distributed systems, ML infrastructure, and agent-native architecture. You'll work with a talent-dense team experienced in probabilistic ML, accelerated computing, embedded systems, and signal theory.
REQUIREMENTS:
- 4–6+ years of professional software engineering experience with significant time building and running backend/distributed systems in production
- Strong proficiency in at least one of: Python, Go, Rust, or similar systems-capable language; comfort moving between languages
- Hands-on experience with AWS or another major cloud provider (GCP, Azure) — compute, networking, storage, IAM, managed data services
- Experience building or integrating LLM/agent systems: tool use, orchestration frameworks, RAG, evaluation, or comparable work
- Experience serving ML models in production (e.g., Triton, TorchServe, vLLM, custom serving) and reasoning about throughput, latency, and cost
- Deep understanding of APIs (REST/gRPC), async processing, message queues/streaming (Kafka, SQS, Kinesis), and data consistency trade-offs
- Track record of owning systems end-to-end and making architecture decisions with long-lived consequences
NICE TO HAVE:
- Experience with containers and orchestration (Docker, Kubernetes, ECS)
- Background in signal processing, RF, geospatial, or high-volume time-series domains
- Experience with GPU programming or accelerated computing
- Familiarity with regulated or defense-adjacent environments and their security expectations
ITAR REQUIREMENT: Must be a U.S. citizen, lawful permanent resident, protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain required authorizations from the U.S. Department of State.
