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 3GPP-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 3GPP 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 3GPP-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 parameter sweeps, optimize scheduler and link adaptation parameters, explore configuration spaces too large to sweep by hand, maintain regression tests, and triage failures across logs, traces and over-the-air captures.
• Translate research into system requirements and implementation-level specifications, and work with modem, payload, ground, RF, ASIC and Testbed teams through integration, field trials and link bring-up, root-causing gaps between simulation, implementation and over-the-air behavior in a fast-paced environment.
• Represent Amazon Leo in 3GPP and other standards development organizations, develop and defend contributions on NTN and D2D work items, and contribute patents and publications.

BASIC QUALIFICATIONS

- Master's degree in Electrical Engineering, or experience working in electrical engineering field

- 7+ years professional experience working in wireless communications at the system or implementation level

- Demonstrated experience in radio resource management for 3GPP systems (LTE, 5G NR or NTN): MAC scheduling, link adaptation, power control, HARQ, mobility or QoS

- Experience developing and running link-level or system-level wireless simulators in Matlab, Python or C++, using agentic AI as a tool for development, test and debugging

PREFERRED QUALIFICATIONS

- PhD with emphasis in wireless communication systems, radio resource management, or network optimization. Strong foundations in algorithm development, optimization techniques, queueing theory, communications theory and machine learning.

- Deep understanding of 3GPP radio resource management: MAC scheduler design, AMC and outer loop link adaptation, open and closed loop power control, HARQ and RLC ARQ interaction, DRX, bandwidth part management, measurement and mobility procedures, and multiple access systems such as OFDMA, SC-FDMA and TDMA.

- Expertise in multi-antenna and beam-domain resource allocation: beam assignment and spatial scheduling, beam coloring and reuse planning, beam measurement, switching and handover, MU-MIMO user pairing, and optimization or machine learning applied to joint allocation.

- Experience in the development and standardization of 3GPP technologies such as LTE, 5G NR, NB-IoT and NTN, including the Rel-17 through Rel-19 NTN and D2D work items, contributions in RAN1 through RAN4, and a track record of publications or patents.

- Experience with direct-to-device or mobile satellite service systems, including the link budget and capacity consequences of a low-gain handset antenna in L/S band MSS or shared MNO spectrum.

- Deep understanding of modem L1/L2 algorithms, QoS in packet based wireless systems, and end-to-end architecture from the physical layer to the application end-point, including RRC, NAS, 5GC/EPC and transport over high-latency links.

- Depth in simulator design: traffic modeling, PHY abstraction, large-scale Monte Carlo evaluation, calibration against field or testbed measurements, and agentic AI used to scale sweeps, maintain regression coverage and triage failures; familiarity with optimization, queueing theory or machine learning applied to resource allocation.

- Ideal candidate has depth in radio resource management and the MAC/RRC layers, a passion for building carrier-grade communication systems, a habit of using agentic AI without giving up rigor, and the ability to work in a small team and lead technical initiatives.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, CA, Sunnyvale - 183,000.00 - 247,600.00 USD annually

USA, TX, Austin - 159,200.00 - 215,300.00 USD annually

USA, WA, Redmond - 159,200.00 - 215,300.00 USD annually