Senior Computer Vision Engineer - C++

Role Summary

You will build and own the real-time vision core of our sorting line: multiple synchronised camera streams per lane, GPU inference, and a per-item decision that must be made before the garment physically reaches the diverter. This is a deep engineering role on the hot path C++, concurrency, memory and GPU throughput where the target is not accuracy alone but accuracy delivered inside a fixed time budget, continuously, for a full shift.You will work to throughput and latency budgets and contribute to the architecture of the edge tier. This is a greenfield build on real hardware you will work on the line itself, with the cameras, lighting and conveyor, alongside the ML team whose models you deploy. The throughput you achieve is not just your result: it sets the rate the rest of the platform is designed around.

Responsibilities

• Image processing pipeline own everything upstream of inference: intrinsic/extrinsic calibration and lens-distortion correction, homography from image space to the belt's encoder coordinate frame, flat-field and white/dark-reference correction, RGB↔hyperspectral spatial registration, background subtraction and connected-component/morphological segmentation to extract per-item ROI.
• Real-time C++ core multi-camera capture and processing under a strict per-item latency budget: concurrency, thread and queue design, memory and buffer management, zero-copy where it counts.
• Multi-camera streaming synchronised capture across cameras and lanes, frame timing and alignment, handling dropped frames and degraded sensors without stalling the pipeline.
• GPU inference integration CUDA/TensorRT, model export and optimisation (ONNX, quantisation), batching, and keeping the GPU fed rather than stalled. Item identity \& tracking assign identity at detection and carry it through classification torouting, using spatial/encoder-based tracking rather than fragile timing windows.
• Performance engineering profile, measure and defend: know where every millisecond goes, and produce numbers, not impressions.
• Edge deployment \& operations run reliably on GPU-backed edge devices (NVIDIA Jetson class) on the factory floor: startup, recovery, remote diagnostics, versioned rollouts, and behaving sensibly when something upstream fails.
• Vision pipeline quality work with the ML team on model integration, and build the
• instrumentation that shows what the line is actually doing in production, not just what the model scored in training.
• Architecture contribution shape the edge tier's design with the team and hold the interface contract between the vision core and the rest of the platform.

Required Skills

Listed in order of expected depth expert command of the core, hands-on proficiency in the rest, and tooling you can pick up here.

CORE EXPERTISE - expert depth required

●​ Computer vision \& image processing fundamentals image formation, camera calibration

and multi-camera registration, colour spaces and radiometric correction, segmentation and morphological operations, and the judgement to know when a classical technique beats a network on the hot path.

●​ Modern C++ (17+) and performance engineering multithreading and concurrency, lock and queue design, CPU and memory awareness, and real profiling experience (perf, Nsight, or equivalent). You have made a real system measurably faster and can explain exactly how.

●​ GPU inference in production CUDA and TensorRT, model conversion and optimisation, batching strategy, and debugging the pipeline when the model runs but the output is wrong.

●​ Multi-camera / multi-stream video pipelines GStreamer or DeepStream, OpenCV, industrial cameras (GigE Vision, GenICam a strong plus); synchronisation, buffering and backpressure across concurrent streams.

●​ Linux \& edge strong fundamentals, Bash, and production operations on GPU-backed edge devices.Join us to be at the forefront of computer vision innovation! Bring your expertise in AI, deep learning, signal processing, and software development to create impactful solutions that shape the future of technology.

WORKING PROFICIENCY - hands-on, used regularly

• Python for tooling, evaluation and model work; PyTorch and ONNX exportgRPC + Protocol Buffers, message queues, streaming interfaces
• Docker, Git, GitHub Actions; scripted, repeatable deployment
• Real-time system design fundamentals latency budgets, buffering, graceful degradation
• AI-assisted development: skilled with coding agents and LLM-based workflows (Claude Code, Codex) in everyday engineering without dropping the bar on correctness, tests or performance

WAYS OF WORKING - how you operate

• Measure, don't assume you quote latency and throughput numbers, and you can say how you obtained them.
• High engineering standards code review, meaningful tests including load and latency tests on the hot path, reliable CI/CD.
• Communication explains technical trade-offs clearly to engineers and to non-technical colleagues and in management.
• Start-up mindset fast-paced environment, shifting requirements, broad ownership, and comfortable working next to real hardware.

Work Location: In person