Senior Systems Modeling Engineer, Beam Planning & Network Optimization

BLUE ORIGIN•Seattle, WA

7d•$145,188 - $203,263•Onsite

About The Position

At Blue Origin, we envision millions of people living and working in space for the benefit of Earth. We’re working to develop reusable, safe, and low-cost space vehicles and systems within a culture of safety, collaboration, and inclusion. Join our team of problem solvers as we add new chapters to the history of spaceflight! Blue Origin is pioneering the future of space-based communications with TeraWave, a revolutionary satellite communications network designed to deliver symmetrical data speeds of up to 6 Tbps anywhere on Earth. This multi-orbit constellation will consist of optically interconnected satellites in low Earth orbit (LEO) and medium Earth orbit (MEO), providing enterprise-grade connectivity for critical operations worldwide. As a member of the Integrated System Modelling team within the TeraWave Chief Systems Architecture office, you will develop, own, and maintain the algorithms and analytical models that underpin TeraWave's beam planning, data routing, and network resource assignment. Your algorithms will directly inform architecture decisions on how thousands of satellites allocate capacity to users, route data across the constellation, and adapt in real time to shifting demand — translating customer service requirements and physical constraints into tractable optimization problems solved at constellation scale. TeraWave is a satellite communications network designed to deliver symmetrical data speeds of up to 6 Tbps anywhere on Earth. This network will service tens of thousands of enterprise, data center, and government users who require reliable connectivity for critical operations. As a large-scale low Earth orbit constellation operating thousands of satellites across multiple orbital shells, TeraWave must solve resource assignment problems of extraordinary combinatorial complexity — from deciding which beams illuminate which ground cells at each moment in time, through routing data across a dynamic optical inter-satellite link mesh with thousands of nodes and time-varying topology, to assigning satellites to gateways and managing handovers as the constellation moves overhead. This is a hands-on algorithm development and modelling role. You will design, implement, and validate the heuristic and algorithmic approaches — in MATLAB, Python, and purpose-built tools — that produce feasible, high-quality solutions to TeraWave's network resource optimization problems. Your work will feed directly into the Network Software Organization, who will translate your algorithms into production systems. You will work within the Integrated System Model framework alongside engineers modelling power, communications, and orbital performance, ensuring that beam planning and routing algorithms are tightly coupled to the broader system picture — respecting power constraints, thermal limits, orbital geometry, and regulatory obligations rather than optimizing in isolation. As part of this team, you will also contribute to the broader modelling of TeraWave's constellation performance — including its ability to maintain consistent customer service through progressive deployment phases, satellite failures, and orbital manoeuvres — bringing the network optimization perspective into system-level figures of merit and ensuring that resource assignment trades are evaluated in the context of their impact on end-to-end service delivery. Successful candidates will bring strong foundations in algorithm design for scheduling, routing, and resource allocation problems at scale, combined with the software development skills to build maintainable, validated, and accessible models that the broader team can use and trust. If you share our passion for building Earth's most capable communications network, thrive in a dynamic startup environment, excel on integrated cross-functional teams of individuals, are experienced and skilled in several relevant areas listed below, continuously seek out opportunities to learn and improve, and do not allow title or job description to limit the depth or breadth of your contributions, then we would love to hear from you.

Requirements

Bachelor's degree in computer science, electrical engineering, aerospace engineering, operations research, applied mathematics, or equivalent technical field; master's degree preferred.
5+ years of experience developing algorithms for scheduling, routing, resource allocation, or combinatorial optimization problems at scale, with demonstrated hands-on implementation and validation experience.
Proficiency in MATLAB and/or Python for scientific computing and algorithm development, with demonstrated ability to develop, validate, and maintain engineering analysis tools — not merely use existing ones.
Strong foundations in graph algorithms, heuristic search, and scheduling techniques, with the ability to formulate real-world engineering problems as tractable algorithmic problems and evaluate solution quality.
Working knowledge of wireless or satellite communications resource management concepts including beam scheduling, frequency reuse, link budgets, or capacity allocation.
Familiarity with time-varying network routing problems, including dynamic graph representations and algorithms that exploit predictable topology changes.
Ability to clearly document algorithmic designs, assumptions, complexity analysis, and performance results in written reports suitable for cross-team consumption and production handoff.
Ability to work collaboratively in a fast-paced, dynamic work environment.

Nice To Haves

Master's or Ph.D. in computer science, operations research, electrical engineering, or a closely related field with thesis work relevant to network optimization, resource scheduling, or algorithm design for large-scale systems.
8+ years of experience spanning multiple areas of network resource optimization including beam planning, routing, scheduling, and capacity management.
Experience with large-scale LEO constellation programs and the unique algorithmic challenges they present — including time-varying topologies, Doppler-shifted links, contact-window-constrained routing, and demand patterns that shift with the Earth's rotation beneath the constellation.
Experience with heuristic and metaheuristic optimization techniques (simulated annealing, genetic algorithms, tabu search, ant colony optimization) and understanding of when these approaches outperform exact methods at scale.
Familiarity with reinforcement learning or online optimization approaches for adaptive resource management in dynamic environments.
Experience with graph-theoretic modelling of communication networks, including flow optimization, network coding, and multi-commodity flow problems.
Knowledge of satellite communications systems including link budget analysis, antenna beam patterns, frequency planning, and interference management.
Experience developing algorithms that must run within real-time or near-real-time computational budgets, including techniques for warm-starting, incremental re-optimization, and solution quality guarantees under time pressure.
Familiarity with version control (Git), collaborative development practices, and software engineering principles as applied to engineering analysis tools.
Experience translating algorithmic research into production system specifications, including defining APIs, performance contracts, and test frameworks that bridge the gap between prototype and deployment.

Responsibilities

Develop, own, and maintain beam-to-ground-cell allocation algorithms for the TeraWave constellation, including demand-driven beam steering strategies, dwell time optimization, and multi-beam scheduling that maximizes throughput subject to power, thermal, and interference constraints.
Design and implement data routing algorithms for the optical inter-satellite link (OISL) mesh, including shortest-path and latency-constrained routing over time-varying network topologies with thousands of nodes, load balancing across parallel paths, and graceful rerouting under link failures or orbital geometry changes.
Develop gateway-satellite assignment algorithms that optimize ground segment utilization, minimize handover frequency, and maintain service continuity as the constellation passes overhead, accounting for gateway capacity limits, weather-driven link margin variation, and geographic requirements.
Design handover algorithms that manage satellite-to-satellite and gateway-to-gateway transitions with minimal service disruption, including predictive handover scheduling that exploits the deterministic orbital geometry of the constellation.
Develop time, frequency, and power resource scheduling algorithms that allocate shared constellation resources across competing demands while respecting per-satellite and per-beam power budgets, regulatory emission constraints, and co-existence obligations.
Build and maintain validated algorithmic tools in MATLAB and/or Python that are version-controlled, documented, and accessible to the broader engineering team, ensuring reproducibility of results and enabling rapid trade studies as the design evolves.
Perform scaling and computational complexity analysis of proposed algorithms, demonstrating feasibility at full constellation scale (3000+ satellites) and identifying where approximations, decomposition, or hierarchical approaches are needed to achieve tractable run-times.
Integrate network optimization models within the broader Integrated System Model, ensuring that beam planning, routing, and resource assignment algorithms operate on consistent orbital mechanics, power budgets, and link budget assumptions shared across the modelling framework.
Perform trade studies quantifying the network performance implications of constellation design choices — including orbital altitude and inclination selection, number of OISL terminals per satellite, gateway placement, and phased deployment ordering — providing algorithm-backed recommendations to inform architecture decisions.
Work closely with the Network Software Organization to translate algorithmic designs into specifications suitable for production implementation, including defining interfaces, performance requirements, and acceptance criteria that ensure the implemented system faithfully reflects the algorithmic intent.
Collaborate with communications, power, thermal, and operations teams to incorporate evolving design constraints into optimization formulations and to ensure algorithm outputs respect physical system limits.