Computer Vision / ML Engineer (Industrial Inspection)

Itransition Group; Northern (KY); Hybrid; USD 90,000 - 150,000; Full time

Benefits offered by this job

Flexible working hours

Remote options

Medical insurance

English courses online

Gym membership

5 days paid sick leave

Job summary

Itransition Group seeks a Computer Vision / ML Engineer to join a small core team on an X-ray inspection concept for wind turbine blades. You will define labelling protocols, build and test detection rules, and ensure reproducible pipelines and exports for the final report.

The role emphasizes clean, testable Python, scalable processing of gigapixel images, and collaboration with an NDT expert. Remote-friendly, with a short-term lab project leading to productization.

Qualifications

• 3+ years in computer vision or applied ML with real images.
• Proficient Python with NumPy, OpenCV, and pandas; build small tools.
• Experience labelling: protocol, inter-annotator checks, export formats.
• Proper evaluation using recall, precision, false positives per unit, non-leaky splits.
• Experience with large images: tiling, coordinates across tiles.
• Unit and integration tests with pytest; reproducible runs with versioned inputs, parameters and outputs.
• One project where you learned a new domain quickly; working English.

Responsibilities

• Write the labelling protocol with the senior engineer and the NDT expert.
• Implement, test, and document the defect rules; for the first defect: a break is a gap in the traced conductor longer than a threshold.
• 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.
• Preserve coordinates correctly across tiling, cropping, stitching and report export, with automated tests.
• 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.

Skills

Python

NumPy

OpenCV

Pandas

Labelling protocols

Image tiling

Testing (pytest)

English

Tools

Streamlit

Dash

Gradio

Job description

## DescriptionWe 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.office remoteEuropean UnionGeorgiaKazakhstanUzbekistan### 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### 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### We offer* Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota* Competitive compensation that depends on your qualification and skills* Career development system with clear skill qualifications* Flexible working hours aligned to your schedule* Options to work remotely* Corporate medical insurance covering services of private and public medical centers* English courses online* Corporate parties and events for employees and their children* Internal conferences, workshops and meetups for learning and experience sharing* Gym membership compensation* 5 days of paid sick leave per year with no obligation to submit a sick-leave certificate