RF Foundation Models ML Researcher

Distributed Spectrum

New York (NY)

Vor Ort

USD 120.000 - 160.000

Vollzeit

14 Tage+

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Benefits dieser Stelle

Above-market salary
Early Series A Equity
Excellent health, dental, and vision coverage
401(k) match
Flexible PTO
Daily office lunches in NYC

Zusammenfassung

Distributed Spectrum in New York, NY is hiring a Machine Learning Researcher to develop AI-enabled sensing systems focusing on RF domains. The successful candidate will work on formulating ML problems, designing experiments in realistic conditions, and optimizing models for deployment. A strong mathematical background, hands-on experience with ML frameworks, and adaptability are key qualifications. The role offers above-market salary, equity, benefits, and a collaborative team environment.

Qualifikationen

  • Comfort operating with ambiguity and changing requirements.
  • Strong instincts for signal-rich data and problem formulation.
  • Experience with RF or signal-centric ML is a plus.

Aufgaben

  • Formulate new ML problems in RF sensing and spectrum understanding.
  • Design experiments for real operating conditions.
  • Build models for structured and partially observed signal environments.
  • Optimize models for throughput and deployment constraints.
  • Convert research into maintainable and deployable code.

Kenntnisse

Deep mathematical and modeling fundamentals
Strong hands-on experience with modern ML frameworks
Ability to work in structured, non-generic data
Clear technical communication
Cross-functional collaboration

Jobbeschreibung

Distributed Spectrum in New York, NY is hiring a Machine Learning Researcher to develop AI-enabled sensing systems focusing on RF domains. The successful candidate will work on formulating ML problems, designing experiments in realistic conditions, and optimizing models for deployment. A strong mathematical background, hands-on experience with ML frameworks, and adaptability are key qualifications. The role offers above-market salary, equity, benefits, and a collaborative team environment.
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