Computer Vision / ML Engineer (Industrial Inspection)

We are looking for a Computer Vision / ML Engineer to join the two-person core computer-vision team of an X-ray inspection proof of concept for wind turbine blades: six to eight months with a path to a product. Around the core team: a technical lead, a project manager, shared QA, and an NDT expert and a blade engineer part-time.

• The senior engineer is accountable for the method; you are responsible for everything that makes it measurable, reproducible and usable: the labelling protocol and the labelled data, the implementation and tests of the detection rules designed with the senior engineer and the NDT expert, batch processing, metrics, and the tool an expert uses to check each finding. The images are single stitched X-rays of whole blades, tens of thousands of pixels long. The first defect is a broken metal conductor. A second, a gap in an adhesive joint, is a feasibility question that may end in a documented no-go. Labelled data is scarce and you will help create it.
• No X-ray or inspection background is required. We teach the domain. What we need is someone who has already learned an unfamiliar field on a project and delivered, who writes clean, testable Python, and who cares whether a number is true.

Responsibilities:

• Write the labelling protocol with the senior engineer and the NDT expert; label, cross-check, fix, export

• Implement, test and document the defect rules designed with the senior engineer and the NDT expert; for the first defect: a break is a gap in the traced conductor longer than a threshold and not on a stitching seam

• If the feasibility gate passes, implement the agreed detection rule for the specific detectable bondline condition

• Build reproducible batch pipelines with versioned inputs, parameters and outputs, resumable on large images, so every reported result can be recreated

• Preserve coordinates correctly across tiling, cropping, stitching and report export, with automated tests: a wrong blade coordinate is worse than a wrong score

• Calibrate thresholds on reference samples with known defects

• Compute and report metrics per physical defect, per metre of blade, before and after any tuning

• Build the review tool: gigapixel image with the trace and findings overlaid, jump to each finding, export to the report

• Prepare the tooling and data package for independent annotation of the control image by the NDT expert; keep those labels sealed until the pipeline and thresholds are frozen, then import them and run the evaluation

• Investigate every miss and the main false positives with the senior engineer

Requirements:

• 3+ years in computer vision, image processing or applied ML with real images

• Python with NumPy, OpenCV, pandas; comfortable building small tools (Streamlit, Dash, Gradio, or a simple web stack)

• Experience labelling or organising labelling: writing a protocol, checking consistency between annotators, exporting to a training format

• Evaluation done properly: recall, precision, false positives per unit, splits that do not leak between crops of the same physical object

• Experience with large images: tiling and reassembly, with coordinates preserved across tiles, crops and the full image

• Unit and integration tests with pytest or equivalent; reproducible runs with versioned inputs, parameters and outputs

• One project where you learned a new domain quickly and can say what you read and whom you asked

• Working English

Nice to have:

• Segmentation or detection with small datasets, transfer learning

• Any radiography, medical imaging, microscopy, industrial or scientific imaging

• Rule-based and hybrid pipelines: classical detection plus a learned component

• Experiment tracking and reproducible pipelines

• Use of AI coding agents for prototyping and tooling