Machine Learning Research, RF Foundation Models Specialist

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, and we do it together in the office 5 days/week.

Some domains already have standard ML playbooks. RF is not one of them.

Distributed Spectrum is building AI-enabled sensing systems for the radio domain, and we are hiring a Machine Learning Researcher, Specialist to bring modern ML to a problem space where representation, structure, physics, runtime constraints, and deployment realities all matter at once.

This role is designed for a strong generalist researcher who wants genuinely open technical terrain. You will work on problems where signal structure, propagation effects, interference, sparse visibility, and edge deployment constraints all shape what "good" looks like. The job is not just to improve accuracy. It is to formulate the right problem, find the right modeling approach, and get that capability into systems that are used in the field.

You will work across the lifecycle of research and deployment: data and evaluation design, experimentation, model development, release readiness, and iteration based on real-world outcomes. You will collaborate closely with embedded, hardware, and mission teammates, and your work will directly influence how Distributed Spectrum builds machine learning capability as the company scales.

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  Formulate new ML problems in RF sensing and spectrum understanding

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  Design experiments and evaluation approaches that reflect real operating conditions including domain shift, changing interference, and varying sensors and platforms

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  Build models for structured, noisy, and partially observed signal environments

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  Improve robustness across propagation, interference, and low-visibility waveform conditions

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  Optimize models for throughput, latency, and deployment constraints

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  Move promising research into a release path for real systems through proofs-of-concept, realistic validation, and conversion into maintainable, deployable code

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  Use field performance to inform the next generation of models and tooling

• Deep mathematical and modeling fundamentals

• Strong hands-on experience with modern ML frameworks and experimental practice

• Ability to work in domains where problem formulation is as important as implementation

• Strong instincts for signal-rich, structured, non-generic data

• Comfort operating with ambiguity and changing requirements

• Clear technical communication and cross-functional collaboration

• Background in RF or signal-centric ML (spectrum sensing, modulation recognition, or related work) is welcome but not required; we are equally interested in researchers from adjacent domains who have demonstrated strong reasoning on hard signal or sensing problems

• Experience building for constrained inference (quantization, kernel-level optimizations, or similar)

• Evidence of research impact: publications, open-source implementations, or prior work building new architectures that shipped

• 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.