You will own the mathematical and physical foundations of one of the few operational commercial space-based RF geolocation systems in active customer use today. The core problem: estimating the position of RF emitters using time difference of arrival (TDOA), frequency difference of arrival (FDOA), and angle of arrival (AoA) measurements collected by a constellation of low Earth orbit (LEO) satellites. You will formulate estimation problems, develop and validate algorithms, characterize error sources, and drive performance improvements across all three measurement domains. You will inherit a working production geolocation system. This is not a greenfield research project. The algorithms exist, they run, and they produce results for real customers. Your job is to understand the existing system deeply, identify where performance is limited, and systematically improve it. Over time you will also extend the system with new measurement types and capabilities as mission requirements evolve. This is a hands‑on, iterative role operating on real‑world data with all its imperfections. Calibration is incomplete. Truth data is sparse. Operational constraints require pragmatic engineering tradeoffs. You will spend significant time examining real geolocation outputs, diagnosing performance issues, refining algorithms based on what the data shows, and shipping incremental improvements. The work cycle is not "design an algorithm and hand it off." It is: analyze outputs, identify the limiting error source, develop or refine an algorithmic solution, validate it against data, and repeat. You will work alongside software engineers who handle production implementation and infrastructure; your role is to ensure the algorithms are correct, well‑understood, and continuously improving. You will write code daily. Python is the primary tool for prototyping, simulation, data analysis, and algorithm validation. This is not a pure research position; the expectation is that you are building, testing, and iterating on working code, not producing papers. Key Responsibilities Own and continuously improve TDOA, FDOA, and AoA geolocation algorithms from mathematical first principles through to working prototype implementations. Develop deep understanding of the existing production geolocation codebase. Identify design assumptions, performance bottlenecks, and areas where the underlying math can be strengthened. Reason across the full sensing chain: from collection geometry and onboard constraints through estimation algorithms to operational product performance. Own the end‑to‑end understanding of how system‑level decisions affect geolocation accuracy. Develop and improve calibration approaches for timing, frequency, antenna, and geometry alignment across a multi‑use distributed satellite constellation. Analyze geolocation outputs against ground truth and known emitter positions to identify systematic errors, performance regressions, and improvement opportunities. Model and characterize error sources: satellite ephemeris uncertainty, clock drift, ionospheric/tropospheric propagation effects, multipath, antenna calibration, and receiver noise. Incorporate orbital mechanics into signal models, accounting for satellite motion, Doppler dynamics, and constellation geometry. Conduct performance analysis: derive theoretical bounds, run Monte‑Carlo simulations, and validate against real satellite data. Translate validated algorithm improvements into specifications that software engineers implement in production systems. Review those implementations for correctness. Add new capabilities as mission requirements evolve: new measurement types, new constellation geometries, new operating conditions. Investigate and resolve anomalies in geolocation outputs by tracing errors back through the signal processing and estimation chain. Document algorithms, assumptions, and performance characteristics with sufficient rigor for defense customer technical review. Required Qualifications Advanced degree (MSc or PhD) in electrical engineering, physics, applied mathematics, aerospace engineering, or a closely related field, with thesis or research work in estimation theory, statistical signal processing, or a related discipline. Strong mathematical foundation in estimation and detection theory, linear algebra, probability, and optimization. Demonstrated ability to go from problem formulation to working code. Python proficiency required; you will prototype algorithms, run simulations, and analyze data in Python daily. Comfort working with imperfect real‑world datasets where calibration is incomplete, truth data is sparse, and operational constraints demand pragmatic tradeoffs. Comfort with iterative, data‑driven development: you examine outputs, form hypotheses about what is limiting performance, implement fixes, and measure the result. Ability to read and understand an existing algorithmic codebase built by someone else, and to work within and improve that system rather than rewrite it. Understanding of, or demonstrated ability to rapidly learn, RF propagation physics and the signal models underlying TDOA, FDOA, and AoA estimation. Familiarity with orbital mechanics concepts sufficient to incorporate satellite position and velocity into geolocation models. Ability to read, understand, and critically evaluate published research in signal processing and geolocation. Additional Qualifications Direct experience with TDOA, FDOA, AoA, or hybrid geolocation techniques. Background in SIGINT, electronic warfare, passive radar, or GNSS signal processing. Experience with SAR, InSAR, or other radar remote sensing (the estimation theory and signal processing fundamentals transfer directly). Experience developing or improving calibration routines for distributed RF systems. C++ reading proficiency sufficient to review and validate production implementations of your algorithms. Prior work in a defense, intelligence, or aerospace context. Experience with spaceborne RF systems, phased array antennas, or LEO satellite constellations. About the Team You will join a small, focused RF geolocation team that includes experienced software engineers handling full‑stack and embedded implementation. Your role is the algorithmic and scientific core. You will work directly with the team lead who brings domain expertise in RF geolocation and defense customer requirements. The team operates remotely across multiple time zones. Spire operates a hybrid work model. This position requires a minimum of three days per week in the office. Access to US export‑controlled software and/or technology may be required for this role. If needed, Spire will arrange the necessary licenses. Salary and Benefits The anticipated base salary range for this position is listed below. Final base salary for this role will be based on the location, skills, experience and qualifications. In addition to base compensation, this role may be eligible for annual equity awards and our employee benefits program, including vacation, sick, and personal time off; optional medical, dental, vision, life, and disability coverage; a Registered Retirement Savings Plan; health and wellness reimbursement program; and participation in Spire’s Employee Stock Purchase Plan. We reserve the right to go outside of the range and/or modify the range or benefits at any time. Canada Salary Range $152,000 — $180,000 CAD Equal Employment Opportunity Spire is a global company and our success draws upon the diverse viewpoints, skills and experiences of our employees. We are proud to be an equal‑opportunity employer and are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or veteran status. All candidates who receive a conditional offer will be required to complete a background check, which may include criminal history and employment verification. #J-18808-Ljbffr