Job VC

We are looking for a Computer Vision Engineer with strong experience in deep learning and applied computer vision systems.
The role involves working on challenging CV problems in real-world environments.
We consider Middle and Senior engineers, with scope adapted to experience.
Responsibilities
Develop, train, and optimize deep learning models for:
image retrieval
image matching (keypoint detection and matching)
auxiliary perception tasks supporting the main pipeline
Evaluate models using quantitative metrics, including:
retrieval quality (mAP, Recall@K)
matching performance (precision/recall, repeatability)
end-to-end system metrics (accuracy, latency, robustness)
Optimize models for production deployment using modern toolchains:
ONNX / TensorRT / OpenVINO / edge acceleration where applicable
model compression techniques (quantization, pruning, distillation)
latency, memory, and throughput optimization
Work with large-scale visual datasets and descriptor-based representations
Collaborate with engineering teams to integrate models into production systems
Required Qualifications
2–4+ years of experience in Computer Vision / Deep Learning
Hands-on experience with keypoint detection and matching models (e.g. SuperPoint, R2D2, DISK, LightGlue, SuperGlue)
Experience with image retrieval or metric learning systems
Strong understanding of geometric and motion-related computer vision concepts:
keypoint detection and description
image matching and geometric verification (RANSAC, homography, PnP)
pose estimation and refinement techniques (PnP, bundle adjustment, pose graph optimization)
optical flow and frame-to-frame tracking
vector search / ANN methods for descriptor retrieval
Strong Python skills (PyTorch and scientific computing stack)
Ability to read and understand inference code in C++
Nice to Have
Experience with noisy or imperfect real-world datasets
Self-supervised or unsupervised learning methods (contrastive learning, homography supervision, etc.)
Experience optimizing models for edge deployment (quantization, pruning, distillation)
Familiarity with FAISS or similar vector search systems
Experience with optimization libraries for geometric problems (bundle adjustment, pose refinement)
Understanding of real-time constraints (latency, memory, CPU inference budgets on ARM or low-power x86 systems)