Work on the development of robust perception and AI systems for industrial and robotic applications
Confidently operate at the intersection of research and engineering
Train models independently
Translate cutting-edge research into prototypes
Integrate them into production-ready systems
Apply and further develop classical and deep learning–based computer vision methods
Independently implement, train, and debug models using PyTorch
Optionally contribute NLP expertise in addition to computer vision (T-shaped profile preferred)
Work with real industrial camera systems (e.g., Lucid or comparable), if applicable
Use Docker for development and deployment workflows

3+ years of relevant professional experience or equivalent project experience (e.g., Master’s thesis in a deep learning–focused environment)
Strong background in classical and deep learning–based computer vision
Excellent proficiency in PyTorch (independent implementation, training, and debugging of models)
Proven ability to translate research into functional prototypes and production-ready solutions
Experience working with real industrial camera systems (e.g., Lucid or comparable) is a plus
Solid understanding of machine learning fundamentals and the ability to critically evaluate research papers
Experience with NLP and Large Language Models (LLMs) is a plus (T-shaped profile preferred)
Strong interest in research and motivation to stay up to date with state-of-the-art developments
Responsible and transparent use of AI tools — leveraging them to accelerate implementation while maintaining ownership of system logic and architecture
Proactive mindset, high quality standards, and strong communication skills for stakeholder collaboration

Work on cutting-edge AI systems for real-world industrial and robotic applications
High level of ownership and responsibility in technically challenging projects
Strong connection between research and real-world deployment
Opportunity to work with state-of-the-art technologies in Computer Vision, LLMs, Graph Systems, and Agentic AI
Flexible working hours and hybrid work options
Modern hardware and high-performance computing infrastructure
Flat hierarchies and fast decision-making processes
Collaborative, innovation-driven team culture

AI Engineer

Key skills