Who we are looking for:

Monk builds visual expertise through Computer Vision and Deep Learning. Our models detect and classify vehicle damage from smartphone photos, and are already used by leading international players, including our parent company ACV Auctions (USA), Toyota Financial Services (France), CAT Logistics (Europe), Getaround (France), Autobiz (Europe), and Tesla (Global).

We’re building a world-class damage detection system, shipped to production and used at scale. We’re looking for an experienced Applied AI Team Lead (Computer Vision) to join our R&D team, drive model performance, and help us build the next generation of deep learning systems.

Join us to shape the future of vehicle damage detection.

What you will do:

• Lead and mentor a small team of ML engineers and data scientists (technical guidance, reviews, best practices, growth)

• Partner with Product and Engineering to shape the model roadmap, and own delivery from problem framing to measurable outcomes

• Drive applied Computer Vision research to improve model accuracy, robustness, and generalization in real-world conditions

• Design, train, and iterate deep learning models (detection, instance segmentation, semantic segmentation, classification)

• Guide datasets construction in contact with data engineering and annotation teams

• Own end-to-end AI projects, from exploration to deployment, monitoring, and retraining lifecycle

• Work closely with Product, Backend, and Infra to ship models to production (API integration, performance, reliability, cost)

What you will need:

• 6+ years of experience in applied Machine Learning / Data Science / Computer vision required, with at least 2+ of those years leading or mentoring engineers/data scientists

• Strong knowledge of modern deep learning architectures and practices, with hands-on experience in object detection, instance segmentation, semantic segmentation, and image classification

• Strong Python skills, comfortable writing, reviewing, and optimizing PyTorch code

• Proven ability to solve real business problems with ML, and to own outcomes, not only experiments

• Fluent in English

• Deep Computer Vision background, including classic 2D/3D approaches (keypoints, optical flow, geometry, curve detection, etc.)

• Comfortable with SQL and working with relational data

• Strong experience with ClearML or NumPy

• HR interview (30 min) 

• Technical interview 1 (90 min) 

• Technical interview 2 (90–150 min) 

• Team discussion (30–60 min)