[Hiring] Computer Vision Engineer @Sparix Global.

Role Description

We are hiring a Computer Vision Engineer to own automated device grading — detecting and scoring cosmetic damage (scratches, dents, cracks, screen defects) from photos and video captured by customers on their own phones, in uncontrolled lighting, on highly reflective surfaces.

This is a founding computer vision role. You will be the first dedicated CV engineer at owning the problem end to end:

• Dataset and annotation strategy
• Detector training and tuning
• Production inference
• Closed-loop feedback that improves the model with every device it sees

Grading is a hard detection problem, not a demo. A hairline crack is a handful of low-contrast pixels; a ceiling-light reflection on glossy glass looks exactly like a scratch. We need someone who has fought these problems in production, not only on clean benchmark datasets.

Qualifications

• 4–5 years of professional computer vision experience, with detection models running in production
• Extensive, hands-on YOLO experience — trained, tuned, and deployed YOLO detectors on your own data
• Demonstrated small-object and low-contrast detection work
• Strong augmentation and hard-negative strategy for uncontrolled capture conditions
• Strong Python and PyTorch skills
• Proven production deployment experience
• Experience building and maintaining a closed-loop feedback system
• Evaluation discipline
• Self-directed and comfortable as the first and only CV specialist
• Able to work with backend engineers to get models into production

Requirements

• Train, tune, and deploy YOLO-family detectors as the backbone of our grading pipeline
• Solve hard detection cases: small-object and low-contrast defects
• Benchmark YOLO variants against alternatives and justify choices
• Own dataset strategy: collection, curation, class balance
• Define the annotation rubric and measure inter-annotator agreement
• Build augmentation that reflects reality
• Version datasets and maintain a trustworthy held-out test set
• Take models from experiment to production
• Instrument models in production
• Partner with backend engineers to integrate inference into platform flows
• Build and maintain a closed-loop system
• Run retrain, evaluate, and redeploy as a routine cycle
• Set the technical direction for computer vision

Benefits

• Competitive salary
• Remote work flexibility
• Opportunities for professional development
• Collaborative work environment