# David Gurevich > Engineering @ Radar | CS @ UWaterloo Location: New York, New York, United States Profile: https://flows.cv/davidgurevich I deliver solutions to complex problems. I am focused on high-speed systems, data-driven applications, and algorithmic problem solving. I’ve worked across embedded radio signal processing, geospatial infrastructure, and systems involving ML and mathematical modelling. I'm excited by projects that bridge low-level engineering and a strong focus on end user experience and product development. ## Work Experience ### Software Engineer @ Radar Jan 2025 – Present | New York, NY Search systems, geocoding, place visit detection, sensor fusion, machine learning ### Software Engineer Intern @ Radar Jan 2024 – Jan 2024 | New York, New York, United States Worked primarily on HorizonDB, Radar's proprietary geocoder, written in Rust. Developed algorithmic optimizations that improved search performance by up to 99% in some cases, reducing latency from hundreds of milliseconds to sub-millisecond ranges. Built data pipelines using Apache Spark and implemented net-new search functionality for street-level geocoding, enabling Radar to unlock significant growth opportunities in international markets. Consulted on firmware development for Radar's new indoor geolocation solution, designing mathematical algorithms for signal processing and location estimation in Python. Worked closely with the GTM team to design statistical tests demonstrating the effectiveness of Radar’s products to clients, providing best practices for measuring efficacy and showcasing improvements gained from switching geocoder providers. ### Undergraduate Researcher @ University of Waterloo Jan 2024 – Jan 2024 | Waterloo, Ontario, Canada Undergraduate Student Researcher in the Applied Math department. Worked with N. Sri Namachchivaya on non-parametric quickest-change detection methods. Investigated geometric methods for detecting changes in unknown probability distributions in an online regime. Worked with David Del Rey Fernandez and Roberto Guglielmi on investigations regarding the use of hardware Spiking Neural Networks (SNNs) for solving PDEs. Hardware SNNs have the potential to be used as low-power accelerators for conventional computers, particularly for Monte Carlo simulations of complex phenomena. ### IoT Research Developer @ Siemens Jan 2024 – Jan 2024 | Waterloo, Ontario, Canada Performed research on localization methods for Bluetooth Low Energy (BLE) emitters in dense indoor sensor networks. Research involved probability theory, convex optimization methods, estimation methods (via Kalman filtering and particle filtering), as well as machine learning using TensorFlow. Designed and implemented machine learning-based fingerprinting method for localization in noisy environments. Made improvements to real-time location services API. Developed and integrated a more robust Kalman filter using Python, MongoDB, and Redis. ### Software Engineer in Test Intern @ MathWorks Jan 2023 – Jan 2023 | Glasgow, Scotland, United Kingdom Contributed to the development of MATLAB Wireless Testbench, a tool facilitating rapid prototyping of software-defined radio systems. Implemented process improvements that reduced the number of FPGA bitstreams needed for building and testing Wireless Testbench by 50%, resulting in significant time (several hours) and computational resource savings. Enhanced the high-speed data acquisition interface (C++) for accurate performance benchmarking, ensuring optimal system evaluation. Made valuable improvements to the open-source UHD simulator (Python), enabling cost-effective testing without expensive hardware. These enhancements improved the simulator's versatility, allowing for dynamic re-routing of RFNoC radio signal processing blocks during tests. ### Software Engineer Intern @ Microchip Technology Inc. Jan 2022 – Jan 2022 | Toronto, Ontario, Canada Responsible for porting and documenting SmartHLS FPGA high-level synthesis build system from Makefiles to Python, allowing for Windows compatibility and improved maintainability. Implemented improvements and developed data structures in C++ which eliminated non-determinism in LLVM-based synthesis from C/C++ to Verilog, allowing for improved integration testing. ### Embedded Software Developer @ Applied Mind, Inc Jan 2022 – Jan 2022 Developed multilateration solution in Python by combining TDOA measurements and Kalman Filter. Implemented high-speed data streaming application for embedded system in Rust within soft real-time constraints. ### Embedded Software Developer @ Applied Mind Jan 2021 – Jan 2021 | Toronto, Ontario, Canada Responsible for the development of a software defined radio (SDR) system in Rust capable of receiving and processing LTE signals at over 60 MS/s. Implemented Linux userspace drivers in Rust and C. Designed and deployed custom Continuous Integration workflow for embedded software using GitHub Actions. ### Software Engineer @ Research in Flows, Inc Jan 2018 – Jan 2020 | Toronto, Canada Area Responsible for research, architecture, and development of the data acquisition and processing pipeline for digital signal processing systems for industrial applications. Extensive use of Python (NumPy, Pandas, Matplotlib, Jupyter Notebooks), C++, and MATLAB. ### Research Assistant @ York University Jan 2019 – Jan 2019 | Toronto, Canada Area I worked on Dr. Jane Heffernan's team to model the spread of infectious diseases in secondary schools. I developed both mathematical and computational models that were analyzed in order to predict disease progression. Lots of MATLAB, Python, and C++. ## Education ### Hons. Bachelor of Mathematics in Computer Science, Computational Mathematics University of Waterloo Jan 2020 – Jan 2025 ## Contact & Social - LinkedIn: https://linkedin.com/in/davidgur - Website: https://gurevich.ca - GitHub: https://github.com/davidgur --- Source: https://flows.cv/davidgurevich JSON Resume: https://flows.cv/davidgurevich/resume.json Last updated: 2026-04-01