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Sr. Communication Systems Research Scientist – Radio Resource Management

Job in Davenport, Scott County, Iowa, 52800, USA
Listing for: Amazon Inc.
Full Time position
Listed on 2026-10-03
Job specializations:
  • Engineering
    Systems Engineer, Research Scientist
Salary/Wage Range or Industry Benchmark: 159200 - 247600 USD Yearly USD 159200.00 247600.00 YEAR
Job Description & How to Apply Below
Sr. Communication Systems Research Scientist – Radio Resource Management

Job :  | Amazon Kuiper Manufacturing Enterprises LLC

Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites providing low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communication Systems Research Scientist, this role owns the research and system design of the radio resource management (RRM) and radio access layers of Amazon Leo’s direct-to-device (D2D) system, delivering 3

GPP-compliant service to unmodified commercial handsets.

The Role:

Be part of the team defining the communication system and architecture of Amazon’s direct-to-device wireless network and analyzing its system level performance: beam and cell capacity, spectral efficiency, coverage, latency and service availability. This is a unique opportunity to innovate with few legacy constraints, in a segment where the standard itself is still being written. This role leads the research and system design of radio resource management (RRM) for a 3

GPP Non-Terrestrial Network (NTN), where D2D upends terrestrial assumptions: a power-limited handset with a near-isotropic antenna, very large cells, hopping beams, large time-varying delay and Doppler, and scarce shared spectrum. RRM in time, frequency and spatial domains is the focus, but the role reasons across the stack, from L1/L2 up through RRC, NAS and 5GC interworking. Agentic AI is expected to be a standard part of the work for development, optimization, tests and debugging, with the scientist accountable for the algorithms, models and conclusions.

Export Control Requirement

Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Key job responsibilities
  • Research, design and specify RRM algorithms for Amazon Leo’s 3

    GPP-based D2D system: MAC scheduling, link adaptation, power control, HARQ strategy, DRX, admission and congestion control, and load balancing, mapping 5QI and QoS flow requirements to scheduler behavior across voice, messaging, emergency and data services.
  • Treat beam management as part of joint resource optimization, not a standalone process, optimizing it with band assignment, packet scheduling and user pairing in multi-user MIMO (MU-MIMO).
  • Define the RRM framework for NTN conditions: earth-fixed and earth-moving cells, large time-varying propagation delay, ephemeris-assisted timing and Doppler pre-compensation, extended timing advance, selective HARQ feedback disabling, feeder link and satellite handovers, and interference and spectrum sharing across beams, satellites and terrestrial networks using the same MNO spectrum.
  • Design mobility and service continuity for a network where the base stations (i.e., satellites) move rather than the user: idle and connected mode mobility, location and time based conditional handover, cell reselection, paging, tracking area design, and NTN-to-terrestrial continuity.
  • Specify supporting L1/L2 elements with the PHY team: numerology under Doppler, PRACH and initial access, coverage enhancement through repetition, synchronization at low SNR, receiver abstraction, and FEC and BLER modeling for link adaptation.
  • Keep the radio design coherent with the networking layers: RRC and NAS, RLC and PDCP over long-RTT links, CU/DU split, NTN gateway and 5GC/EPC integration, and transport behavior.
  • Develop link-level and system-level simulators capturing constellation dynamics, beam patterns, handset characteristics, traffic models and RRM behavior, and use agentic AI across that loop: build and refactor simulation code, scale…
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