Senior Software Engineer, Backend Systems

DS creates systems that power the next generation of radio spectrum intelligence. We collect radio data from all over the world, train neural networks to decipher it, and run them on the smallest chips we can. We’re solving a new, technically hard problem where nothing from other fields works out of the box, and along the way, we’ve built our own stack from scratch, including entirely new embedding model architectures, custom GPU kernels, and much more.

Joining DS means owning major parts of a fast-growing AI research organization, joining a collaborative, talent-dense team with decades of experience in probabilistic ML, accelerated computing, embedded systems, and signal theory, and growing your career in the areas that interest you. You’ll fit in if you want to come to work for the problem itself and don’t want to choose between technical rigor, business value, and real-world impact.

We work with high ownership and trust.

We're hiring a Senior Backend Engineer to design and build the services that sit at the heart of the Distributed Spectrum platform: the systems that ingest spectrum data, orchestrate ML inference, and increasingly, power autonomous agents that reason over what our sensors see. You'll define how our backend evolves from a set of services into an agent-native system — one where models, tools, and workflows are first-class citizens of the architecture rather than bolted-on integrations.

This is a high-ownership role. You'll set technical direction, make architecture decisions that stick for years, and mentor engineers around you.

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  Design, build, and operate backend services that are reliable, observable, and built to scale with our sensor fleet and customer base.

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  Architect the agent layer of our platform: tool-calling interfaces, orchestration, state management, memory, and evaluation harnesses for LLM- and model-driven agents.

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  Own the ML inference architecture — model serving, batching, GPU/CPU scheduling, latency and cost optimization, and the APIs that expose inference to the rest of the system.

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  Define service boundaries, data contracts, and API standards across the backend.

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  Build for production: instrumentation, alerting, graceful degradation, and clear runbooks.

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  Partner closely with ML researchers to move models from experiment to production quickly and safely.

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  Lead design reviews, mentor engineers, and raise the bar on code quality and engineering practice.

• 4–6+ years of professional software engineering experience, with significant time spent building and running backend / distributed systems in production.

• Strong proficiency in at least one of Python, Go, Rust, or a similar systems-capable language; comfort moving between languages.

• Hands-on experience with AWS or another major cloud provider (GCP, Azure) — compute, networking, storage, IAM, and 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, etc.), and data consistency trade-offs.

• Track record of owning systems end to end and making architecture decisions with long-lived consequences.

• Experience with containers and orchestration (Docker, Kubernetes, ECS).

• Background in signal processing, RF, geospatial, or other high-volume time-series domains.

• Experience with GPU programming or accelerated computing.

• Familiarity with regulated or defense-adjacent environments and their security expectations.

• Fast learners over specific backgrounds – We care more about how quickly you can pick up new skills than where you’ve worked before.

• Intellectual honesty – The right answer matters more than being right. You challenge assumptions, test ideas, and pivot when needed.

• Adaptability – We’re organized, but sometimes things change quickly. You find a way to make it work and balance short-term deliverables with long-term goals.

• Ownership of outcomes – You optimize your own time, focus on what matters to deliver quickly, and cut out inefficiencies.

• Not building in a vacuum – You stay connected to the rest of our teams and our customers to make sure all the pieces fit together.