Computer Vision Engineer – Document Forensics & Tamper Detection

Raval West; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Raval West is seeking a Computer Vision Engineer to detect altered and fabricated documents across 20 types. You will craft Python-based checks for images and PDFs, quantify detection reliability, and provide reviewers with clear evidence for each flag.

Location options include Remote (US), Sacramento, or nearby. The role emphasizes building robust detection systems, evaluating models on imbalanced data, and communicating findings to nontechnical reviewers.

Qualifications

• 3+ years in applied computer vision, ML, or fraud detection; at least one model in production.
• Hands-on Python experience with OpenCV or scikit-image, plus PyTorch or TensorFlow.
• Experience inspecting images and PDFs programmatically, including rendering, metadata, and file structure.

Responsibilities

• Build checks for inconsistent fonts, text alignment, image edits, and anomalies in PDF structure or metadata.
• Create a labeled evaluation set using genuine documents, tampered examples, and synthetic alterations.
• Tune detection thresholds with fraud reviewers; measure precision, recall, and false positives at realistic fraud rates and varying image quality.
• Run new checks in shadow mode before they affect review decisions, monitor performance after launch, and show reviewers which region/file detail triggered each flag.

Skills

Computer vision

Fraud detection

Model deployment

Tools

OpenCV / scikit-image

PyTorch / TensorFlow

PyMuPDF / pikepdf / ExifTool

Job description

Seeking a Computer Vision Engineer to detect altered and fabricated documents across 20 document types. You’ll build Python-based checks for images and PDFs, measure how reliably they detect tampering, and give fraud reviewers clear evidence for each flag.

Location: Remote (US), Sacramento or nearby preferred

What you’ll do

• Build checks for inconsistent fonts, text alignment, image edits, and anomalies in PDF structure or metadata.
• Create a labeled evaluation set using genuine documents, confirmed examples of tampering, and synthetic alterations. Test against emerging methods, including AI-generated documents.
• Tune detection thresholds with fraud reviewers. Measure precision, recall, and false positives at realistic fraud rates and across different image-quality levels.
• Run new checks in shadow mode before they affect review decisions, monitor performance after launch, and show reviewers which region or file detail triggered each flag.

Qualifications

• 3+ years for mid-level or 6+ years for senior in applied computer vision, machine learning, or fraud detection, including at least one model deployed to production.
• Hands-on Python experience with OpenCV or scikit-image, plus PyTorch or TensorFlow.
• Experience inspecting images and PDFs programmatically, including rendering, metadata, and file structure.
• Experience evaluating models on imbalanced datasets, selecting thresholds, and explaining findings to nontechnical reviewers.

Experience in document or image forensics, identity verification, or claims fraud is preferred. Familiarity with PyMuPDF, pikepdf, or ExifTool is a plus.