Embedded Computer Vision Engineer (Edge Inference)

Overview

We are building computer-vision capabilities on Linux-based edge devices. This role owns the embedded software that takes models from 'works on a workstation' to 'runs reliably, efficiently, and measurably fast on-device.' You will develop and optimize inference pipelines, integrate vendor runtimes on NPUs/MPUs, and work close to the Linux kernel when needed (performance, memory, I/O, and driver interactions).

What You Will Do

Build and maintain production-grade embedded software for on-device computer vision inference (camera ingest, preprocessing, inference, postprocessing, telemetry) primarily in C++, with Rust as an option where appropriate

Integrate and run deep learning models using edge runtimes/toolchains (e.g., TensorRT, TFLite, OpenVINO, ONNX Runtime, vendor SDKs for NPUs/MPUs)

Profile and optimize end-to-end performance: latency, throughput, memory footprint, power, and thermal constraints

Implement deployment-oriented model optimizations when needed (quantization workflows, operator compatibility fixes, graph optimizations, runtime-specific conversion)

Work on Linux-based embedded platforms: cross-compilation, build systems, packaging, and reliable field deployment

Debug complex system issues across the stack: kernel/user-space boundaries, driver/I/O bottlenecks, memory contention, and multi-threaded performance

Collaborate with model/CV stakeholders to ensure models are edge-ready (I/O specs, accuracy vs. performance tradeoffs, validation on target hardware)

Establish and uphold engineering standards: code quality, test strategy, CI, performance benchmarks, and observability on-device

Requirements

Required qualifications

7-8+ years professional experience in embedded software development, with significant time shipping Linux-based products

Strong expertise in C++ (modern C++11/14/17); Rust experience is a plus (or willingness to use Rust where it benefits reliability/performance)

Strong Linux systems knowledge, including at least some of: kernel fundamentals, device I/O, scheduling, memory behavior, and profiling/debugging tooling (e.g., perf, ftrace, eBPF)

Working knowledge of computer vision and deep learning inference concepts (pipelines, tensors, common CV tasks, latency/accuracy tradeoffs). You do not need to be a model developer/researcher, but must be fluent in deploying and running models

Experience optimizing inference for edge hardware (NPUs/MPUs/GPUs/accelerators), including quantization and runtime constraints

Master's degree minimum in a relevant field (Computer Vision, Machine Learning/Deep Learning, Electrical/Computer Engineering, Computer Science, or related)

Preferred Qualifications

Camera stacks and media pipelines (V4L2, GStreamer, ISP integration)

Embedded build and deployment toolchains (Yocto/Buildroot, CMake/Bazel)

Hardware-aware optimization experience (ARM, NEON/SIMD)

Experience with vendor-specific NPU SDKs and quantization toolchains (e.g., Rockchip RKNN, Qualcomm SNPE/QNN, MediaTek, Intel Movidius, etc.)

OTA, reliability, and embedded security practices (watchdogs, crash dumps, secure boot)

AI coding tools

Comfortable using modern AI-assisted development tools (e.g., code completion, refactoring, test generation) while maintaining strong engineering judgment, code review discipline, and security awareness

Benefits

At Rapsodo, you will have the opportunity to

Work on cutting-edge technology that integrates AI, sensor fusion, and high-performance embedded computing,

Be part of a highly skilled, multidisciplinary engineering team driving innovation,

Lead end-to-end product development with real-world impact,

Shape the future of sports through advanced embedded systems and AI-driven solutions

If you're passionate about solving complex engineering challenges and want to be at the forefront of next-generation technology, we'd love to hear from you.

Apply now and be part of the team that's redefining performance through innovation!