At a Glance
- Tasks: Own and improve RF geolocation algorithms using real-world data and cutting-edge technology.
- Company: Join a dynamic team at a leading aerospace company focused on innovative RF solutions.
- Benefits: Enjoy a hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborate with experienced engineers in a supportive, remote-friendly environment.
- Why this job: Make a tangible impact in the aerospace industry while working with advanced RF technologies.
- Qualifications: Advanced degree in relevant fields and strong Python programming skills required.
The predicted salary is between 70000 - 90000 € per year.
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, and this position will require you to work 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—this is not something candidates need to have before applying.
Principal RF Engineer in Glasgow employer: Spire
At Spire, we pride ourselves on being an exceptional employer, offering a dynamic work environment where innovation meets real-world application. Our collaborative culture fosters continuous learning and growth, allowing you to refine your skills while working on cutting-edge RF geolocation systems that have a tangible impact. With a hybrid work model and a focus on employee well-being, we provide the perfect balance of flexibility and teamwork, making it an ideal place for those seeking meaningful and rewarding careers in aerospace engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Principal RF Engineer in Glasgow
✨Tip Number 1
Get to know the company inside out! Research their projects, values, and culture. This will help you tailor your conversations and show that you're genuinely interested in being part of the team.
✨Tip Number 2
Network like a pro! Reach out to current employees on LinkedIn or attend industry events. Building connections can give you insider info and might even lead to a referral, which is always a bonus!
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and algorithms. Since you'll be working with Python daily, make sure you're comfortable with prototyping and data analysis in that language.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re serious about joining our awesome team!
We think you need these skills to ace Principal RF Engineer in Glasgow
Some tips for your application 🫡
Understand the Role:Before you start writing your application, take a moment to really understand what the Principal RF Engineer role entails. Dive into the job description and make sure you grasp the key responsibilities and qualifications. This will help you tailor your application to show how you fit the bill.
Show Your Skills:When you're crafting your application, don't just list your qualifications—show us how you've used them in real-world scenarios. Highlight your experience with TDOA, FDOA, and AoA techniques, and give examples of how you've tackled similar challenges in the past. We want to see your problem-solving skills in action!
Be Clear and Concise:Keep your application clear and to the point. Use straightforward language and avoid jargon unless it's relevant to the role. We appreciate a well-structured application that makes it easy for us to see your qualifications and experience without wading through unnecessary fluff.
Apply Through Our Website:Finally, make sure to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our company and culture while you’re at it!
How to prepare for a job interview at Spire
✨Know Your Algorithms Inside Out
Make sure you have a deep understanding of TDOA, FDOA, and AoA algorithms. Be prepared to discuss how these algorithms work, their limitations, and how you would improve them based on real-world data. This will show your potential employer that you can think critically about existing systems.
✨Brush Up on Python Skills
Since Python is the primary tool for prototyping and analysis, ensure you're comfortable writing and debugging code. Practice by working on small projects or simulations related to RF geolocation. Being able to demonstrate your coding skills during the interview can set you apart from other candidates.
✨Familiarise Yourself with Real-World Data Challenges
Understand the common issues faced when working with imperfect datasets, such as calibration problems and sparse truth data. Be ready to discuss how you would approach diagnosing performance issues and refining algorithms based on these challenges.
✨Prepare for Technical Questions
Expect to be asked technical questions that assess your knowledge in estimation theory, signal processing, and orbital mechanics. Review relevant concepts and be ready to explain how they apply to the role. This will demonstrate your expertise and readiness to tackle the job's challenges.
