Your Impact
As a Machine Learning Engineer at Dedrone, you’ll play a pivotal role in advancing airspace security. Leveraging cutting-edge camera systems and machine learning algorithms, you will help us detect, track, and classify diverse flying objects in complex airspace environments. By innovating detection and tracking techniques, you’ll be key in developing our systems to respond to evolving airspace threats—from quadcopters and drones to other larger or unconventional aircraft. Working within a highly skilled team, you’ll gain access to world-class resources and an extensive, unique dataset, providing a one-of-a-kind opportunity to shape the future of airspace security and computer vision.
What You’ll Do
- Design and implement high-performance C++ software that runs computer vision and tracking algorithms in real time on edge devices.
- Work closely with computer vision / self-supervised learning engineers to integrate their models into production pipelines, including pre/post-processing, I/O, and system orchestration.
- Build and optimize multithreaded and parallel processing pipelines for ingesting, synchronizing, and processing data from a networked system of cameras.
- Implement and tune CUDA kernels and GPU-accelerated components to maximize throughput and minimize latency for inference, tracking, and search.
- Design robust data structures and memory management strategies for handling large volumes of video, sensor, and metadata streams under tight compute and power constraints.
- Profile and optimize code using tools such as perf, valgrind, nvprof / Nsight, and similar to identify bottlenecks and improve CPU/GPU utilization.
- Collaborate with simulation and CV teams to deploy and evaluate algorithms in realistic test scenarios, including fault handling and performance monitoring.
- Develop clean, well-tested, and well-documented C++ libraries and services that can be reused across products and future airspace applications.
- Contribute to system-level architecture decisions, including inter-process communication, scheduling, resource allocation, and deployment strategies on edge platforms.
What You Bring
- 5+ years of professional experience in modern C++ (C++14/17 or later), with strong object-oriented and generic programming skills.
- Deep understanding of multithreading and concurrency (threads, thread pools, locks, lock-free structures, atomics, futures, async patterns) and experience building robust, concurrent systems.
- Hands-on experience with parallel processing frameworks or patterns (SIMD, task-based parallelism, GPU offload, or similar) for real-time or high-throughput applications.
- Strong command of data structures and algorithms, and the ability to choose and implement the right structures for performance-critical, memory-constrained environments.
- Proven experience with memory management and performance optimization in C++ (stack vs heap, custom allocators, cache-aware design, avoiding fragmentation, RAII, move semantics).
- Practical experience with CUDA (or similar GPU programming frameworks): writing kernels, managing GPU memory, optimizing for occupancy and bandwidth, and integrating with C++ codebases.
- Experience integrating machine learning or computer vision inference engines (e.g., TensorRT, OpenVINO, ONNX Runtime) is a strong plus.
- Familiarity with Linux-based development (build systems like CMake, unit testing frameworks, containerization and/or cross-compilation for edge devices).
- Strong debugging and profiling skills across CPU and GPU, and a methodical approach to benchmarking and regression testing.
- Excellent collaboration and communication skills, with a track record of working closely with research or ML teams to move algorithms from prototype to production.