computer vision engineer in scientific imaging

Itransition develops software solutions and delivers projects for clients such as PayPal, Wargaming, Xerox, Philips, Adidas, and Toyota.

• Assess the first images, determine visibility and contrast, and identify where the method will and will not work;
• Define image-quality requirements for the acquisition partner and check every delivery against them;
• Build preprocessing for tiling, thickness normalisation, denoising, and seam handling;
• Build conductor tracing using linear-structure filters, trace graphs, branch handling, and rejection of look-alike structures;
• Run the feasibility gate for the second defect type using a simulator and reference samples with known defects; document a no-go outcome if required;
• Own the physical and algorithmic hypothesis, pipeline architecture, thresholds, uncertain-case policy, validation design, technical decisions on misses, and technical sign-off;
• Review the second engineer's implementation and keep the end-to-end pipeline reproducible;
• Work with the acquisition partner's engineers and the technical lead on image format, calibration, metadata, and stitching problems;
• Tune the pipeline on the first images from an installed blade and report what changed and why;
• Work with the NDT expert and blade engineer on thresholds and every miss;
• Attend one approximately one-week test campaign at a wind farm to check images before the equipment leaves.

• 4+ Years of experience in computer vision or image processing on real, imperfect images;
• Working knowledge of classical image processing, including filtering, ridge and line detection, morphology, segmentation, connected components, and graph-based tracing;
• Experience processing images too large for memory, including tiling, overlap, and coordinate reconstruction in the full image;
• Strong Python with NumPy, OpenCV, scikit-image, or equivalents;
• Experience designing labelling and validation approaches for scarce data;
• Experience quantifying image quality, including contrast-to-noise, resolution, blur, and repeatability;
• Experience designing controlled experiments that separate imaging limitations from algorithm limitations;
• Experience moving into an unfamiliar imaging modality and explaining how you got up to speed;
• English good enough to read papers and talk to the client's engineers;
• Nice to have: radiography, CT, NDT, composites, weld or semiconductor inspection, physics or engineering background, imaging simulation, small-dataset deep learning, experience influencing physical data acquisition, use of AI coding agents for prototyping and tooling.

• Competitive compensation depends on qualification and skills;
• Flexible working hours aligned to the employee's schedule;
• Remote work options;
• Corporate medical insurance covering private and public medical centers;
• Online English courses;
• Corporate parties and events for employees and their children;
• Internal conferences, workshops, and meetups;
• Gym membership compensation;
• 5 Days of paid sick leave per year without requiring a sick-leave certificate.