Computer Vision Engineer

We are looking for a Computer Vision Engineer to design and implement visual intelligence systems that power personalized digital experiences across connected devices and mobile applications.

This is a hands-on role focused on applied computer vision - from model design and training to on-device deployment. You will work at the intersection of deep learning, image and video processing, and product integration, collaborating closely with backend engineers, ML engineers, and product managers to bring vision-driven features into production and continuously optimize them for real-world usage and resource-constrained environments.

Responsibilities:

• Design, train, and deploy computer vision models for use cases such as object detection, classification, segmentation, and visual analysis;
• Build and maintain data pipelines for image and video processing, including annotation, augmentation, and dataset versioning workflows;
• Optimize models for inference on edge and mobile devices (quantization, pruning, distillation, hardware-accelerated runtimes);
• Collaborate with product and engineering teams to translate product ideas into vision-powered features;
• Implement model observability, monitoring, and retraining workflows for models running in production;
• Contribute to the architecture of a modern ML platform within a cloud-native microservices environment;
• Work with backend and mobile teams to expose model outputs via APIs and integrate them into customer-facing experiences;
• Evaluate emerging CV/AI research and assess promising methods for future implementation.

Requirements:

• 3+ years of experience in applied computer vision or machine learning with a strong CV focus;
• Strong knowledge of:
  • Deep learning for vision: CNNs, object detection and segmentation architectures (e.g. YOLO, Faster R-CNN, U-Net), and modern vision transformers;
  • Python and ML libraries such as PyTorch, TensorFlow, OpenCV;
  • Building, deploying, and maintaining CV models in production.
• Familiarity with:
  • Model optimization and on-device inference (ONNX, TensorRT, TensorFlow Lite, Core ML, OpenVINO);
  • MLOps tools (e.g. MLflow, TFX, Airflow, Vertex AI);
  • Data and video pipelines (e.g. Kafka, BigQuery, FFmpeg, GStreamer);
  • Model serving frameworks (e.g. FastAPI, TensorFlow Serving, TorchServe).
• Experience with cloud platforms (GCP, AWS, or Azure) and container-based deployments;
• Comfortable collaborating with product, engineering, and analytics teams;
• Bonus: Experience with edge AI, mobile ML, multi-modal models, or real-time video processing at scale.

We Offer:

• Remote work and flexible working hours.
• Legal support.
• Paid sick leaves.
• Paid vacations.
• Medical insurance. 
• Free English classes.