Computer Vision Engineer / Data Scientist

Position: Computer Vision Engineer / Data Scientist Level: Senior Experience: 6+ years (NOT freelance) Stack: CV, SSL, Python, PyTorch, TensorFlow Location: Remote / удаленно Employment: Fulltime / полная Timezone: UTC+3 Company: ITM Salary: 250,000₽ – 400,000₽ net

REQUIREMENTS:

1. 6+ years of experience in Computer Vision.
2. Experience in bringing models to production is mandatory.
3. 1+ year of experience with medical images in production: CT, mammography.
4. 3+ years of experience with images in production.
5. 1+ year of experience with document processing / OCR / KYC.
6. 1+ year of experience with biometrics (face, palm, voice, etc.).
7. 1+ year of experience with multimodal models / LLM.
8. 1+ year of experience with fraud detection.
9. 1+ year of experience with video.
10. 1+ year of experience with 3D data (volumes, point clouds, meshes).
11. 1+ year of experience with Self-Supervised / Weakly-Supervised Learning.

WHO WE ARE LOOKING FOR:

1. Fulltime only, no combined employment.
2. We need "working hands", NOT a technical manager.
3. We need someone who has already built complex CV systems, not someone who is learning on the go.

WE EXPECT:

1. Independence.
2. High work speed.
3. Ability to quickly test hypotheses.

WHO WILL NOT BE A GOOD FIT:

1. Candidates with predominant or last experience in technical manager roles – this is irrelevant experience for this position. We are looking for "working hands", not a technical manager.
2. Candidates looking for a second job. Any combined employment will be noticeable within the first 2 months. Honest fulltime is required.
3. Candidates with predominant or last experience in freelance.

TASKS: Development and improvement of KYC pipelines:

1. document image processing
2. validation, classification, data extraction
3. fraud scenario detection (screen, copies, editing, etc.)

Working with multimodal models: 4. CV and LLM integration (Vision-Language) 5. model tuning, retraining, and optimization 6. building agent-based systems around LLMs

Development of CV models: 7. detection, segmentation, classification 8. working with documents, images, video

Development of biometric solutions: 9. face matching 10. palm vein / PalmPay

Building and optimizing ML pipelines: 11. training, inference, validation 12. working with large datasets

Participation in R&D: 13. experiment design 14. rapid hypothesis iteration