Senior C++ Signal Processing Engineer (Radar & RF Systems)

Metrea•Victor, NY

4d•Onsite

About The Position

RADLAB is looking for a Senior C++ Signal Processing Engineer to help build the modeling, simulation, and processing backbone for new radar and RF sensing capabilities. This is a hands-on engineering role for someone who likes turning math into real systems: deriving and reviewing algorithms, implementing performance-critical processing in modern C++, validating results against simulation and measured data with Python, profiling performance, and deploying working code onto hardware that supports real-world missions. You do not need to come from radar. We are interested in engineers and scientists with deep signal-processing experience in adjacent fields such as communications, sonar, medical imaging, SDR, wireless systems, acoustics, or scientific computing, as well as candidates with radar experience who want to apply that background to advanced RF sensing. You may be a fit if you have built C++ simulations, numerical tools, or signal-processing pipelines for noisy sensor data and think deeply about whether the results are physically and mathematically correct. Prior radar experience is helpful, but less important than strong C++, applied math, signal-processing judgment, and the ability to validate results rigorously.

Requirements

BS/MS in Electrical Engineering, Computer Science, Applied Mathematics, Physics, or a related field
6+ years of professional experience implementing and validating signal-processing, modeling/simulation, numerical, or scientific computing algorithms in C/C++, with proficiency in Python for analysis, validation, visualization and test automation. Equivalent depth through graduate research and other applied engineering work will also be considered.
A strong applied math foundation: comfortable reading equations and derivations, understanding modeling assumptions, and translating mathematical specifications into reliable numerical software
Experience validating simulations and algorithm implementations against analytical derivations, simplified test cases, golden vectors, simulated ground truth, independent implementations, or measured data
Software engineering discipline: version control, testing, documentation, code review
Clear technical writing and the ability to communicate assumptions, methods, results, and tradeoffs
A commitment to engineering and mathematical rigor to advance the state of the art in RF sensing
U.S. Citizenship

Nice To Haves

Radar-specific experience is a plus, not a prerequisite. If you've built signal-processing systems in communications, sonar, medical imaging, SDR, wireless systems, acoustics, scientific computing, or related fields and want to learn radar, we want to talk to you.
Modern C++ fluency, including C++17/20, templates, and concepts
Advanced Python for prototyping, analysis, visualization, automated validation, and simulation campaign orchestration
Experience with performance optimization, vectorization, multithreading, OpenMP, CUDA, GPU acceleration, or HPC-style workloads
FPGA experience. HLS (C++-based) is sufficient; RTL is not required. You'll have the opportunity to learn if you haven't done it
Familiarity with SDR platforms or hardware-in-the-loop environments
An MS or PhD with research experience in radar, statistical signal-processing, detection and estimation theory, applied electromagnetics, or a related topic.

Responsibilities

Build and validate scalable C++ modeling, simulation, and signal-processing tools for radar and RF sensing concepts
Develop and validate radar/RF signal-processing algorithms, such as signal detection, parameter estimation, correlation/matched filtering, Doppler processing, digital beamforming, and digital filtering
Own the reference processing chain, including unit and integration tests, and golden vector validation inside our CI/CD pipelines
Run simulation campaigns, including Monte Carlo and sensitivity sweeps, using C++ and Python; document methods, assumptions and results
Profile performance and identify numerical stability issues, bottlenecks, and optimization opportunities
Translate mathematical specifications into correct, maintainable, testable code
Help transition algorithms from simulation/reference form toward RADLAB hardware and assess feasibility under real-time or mission-relevant constraints
Work closely with RADLAB leadership, R&D staff, and hardware and software engineers to turn research concepts into tested, deployable processing capabilities