Data Scientist (Computer Vision)

About Us

We are building next-generation computer vision systems that power real-time understanding of the world through images and video. Our mission is to create scalable, intelligent visual solutions that drive innovation across industries - from autonomous robotics to medical imaging and smart manufacturing.We are a fast-moving, collaborative team that values clean code, thoughtful design, and solving hard problems with practical solutions. If you're passionate about building high-performance CV pipelines that make a real impact, we’d love to meet you.

What You’ll Do

• Design, implement, and optimize computer vision algorithms

• Train and evaluate deep learning models using PyTorch or TensorFlow.

• Build scalable image/video processing pipelines - from raw data to real-time predictions.

• Analyze data quality, perform feature engineering, and run experiments for performance improvement.

• Prepare ML models for deployment and work closely with engineering teams to integrate them into production.

• Develop robust MLOps workflows for model training, validation, and monitoring.

• Write clear technical documentation and participate in code reviews.

Requirements

• 3–4 years experience with machine learning focused on computer vision tasks like detection and segmentation.

• Strong skills in PyTorch and Python, familiar with modern CV models and optimization.

• Practical experience with image/video data preprocessing and quality analysis.

• Able to train, validate, and deploy ML models in production, comfortable with cloud (AWS/GCP) and Linux environments.

• Knowledge of MLOps practices for building and monitoring ML pipelines.

• Good coding standards with clear documentation and peer reviews.

• Independent, proactive, and fluent in English and Polish.

Nice to Have

• Knowledge of model optimization

• Contributions to Kaggle, CVPR/ICCV publications, or open-source projects in CV.

What We Offer

• Competitive salary based on experience (B2B or employment contract).

• Flexible working hours and remote-friendly culture.

• High-end equipment (GPU, multiple monitors, etc.).

• Learning and development budget (courses, conferences, books).

• A talented, cross-functional team working on meaningful real-world problems.