Senior\Tech Lead \Principle Embedded SW Engineer

This is a hands-on senior IC role. You will design subsystems, implement them in modern C++, and be the person who diagnoses the hard, ambiguous problems when a pipeline drops samples, jitters, or starves the GPU.

What you'll do- Design and build the real-time data path that ingests high-rate sensor/A2Ds.

- Port different Algorithms from python to C++.

- Integrate on-device signal processing and inference (GPU/DSP).

- Own subsystems end-to-end: architecture, implementation, profiling, and production support on embedded Linux (Yocto) and NVIDIA Jetson / GPU edge targets. - Profile and optimize CPU, memory, cache, and I/O behavior; eliminate jitter, contention, and copies in the critical path. - Write design docs, defend technical decisions, and collaborate with hardware, DSP/algorithms, and systems teams.

Required qualifications

- 7+ yearsbuilding production embedded / systems software.

- Expert in Modern C++ (C++17/20) — RAII, move semantics, concurrency, and writing high-performance, memory-safe code.

- Proven track record shipping products end-to-end, from design through production deployment and field hardening.

- Hands-on with real-time, high-throughput data pipelines: low-latency ingest, lock-free / concurrent data structures, zero-copy techniques, and backpressure handling.

- Strong on embedded Linux, including building and customizing images with Yocto (or equivalent: Buildroot).

- Experience with signal processing, and porting algorithms into production C++ (e.g. from MATLAB / Python / reference implementations into optimized, real-time embedded code).

- Deep, demonstrable problem-solving and debugging skills on complex, ambiguous systems issues (perf, concurrency, timing, memory). Strongly preferred- Real-time DSP or sensor signal-processing background (radar/RF/A2D sample handling). Nice to have- ZeroMQ(other high-performance messaging) for inter-process and networked transport.

- CUDA/ GPU programming experience on NVIDIA Jetsonor discrete GPUs — kernels, memory management, and integrating GPU work into a real-time pipeline.

- Low-latency video pipeline experience (H.264/H.265 capture/encode/transport). - RTOS / bare-metal experience.

- Defense, aerospace, or other safety-/mission-critical domain experience.

What we look for- Ownership: you take a problem from vague requirements to production grade solution.

- Clear communication of design trade-offs to both software and hardware colleagues..most about engineers who have shipped real products end-to-end — design, implementation, and production hardening.