Flexible hybrid work (3 days remote, 2 days office)
Flexible working hours (start between 7:30-10:00)
Application Deadline:
12.2.2026
We are looking for someone who can lead technically: set direction on hard problems, help others level up, and make decisions when there's no obvious answer. In this role, you define how we approach research challenges and how that research gets implemented into production. You'll have ownership, from picking which ideas are worth pursuing to defining how we build and deploy them into production.
Key Responsibilities
Research & Innovation
Advance ML architectures by designing and implementing novel layers and components for deep learning models
New fine-tuning approaches using reinforcement learning techniques and other advanced methodologies
Conduct original research in domain-specific AI applications (medical imaging, audio separation, NLP)
Experiment with hybrid architectures combining different ML paradigms
Publish findings and contribute to the scientific community through papers and open-source contributions
Technical Implementation
Build and test model architectures through structured experimentation
Implement custom loss functions, optimization strategies, and training pipelines
Build end-to-end ML systems from research prototype to production deployment
Make research ideas work under real-world constraints
Collect, prepare and structure data for model training
Collaboration & Leadership
Contribute to research strategy and roadmap planning
Collaborate with European research institutions on cutting-edge projects
Mentor team members on advanced ML techniques and research methodologies
Present research findings to technical and non-technical stakeholders
Stay ahead of the curve by exploring emerging AI research areas
Required Qualifications
5+ years of professional experience in research or production environments
Degree in Computer Science, Mathematics, Physics, or related field (PMF, FER, FOI or similar)
Strong foundation in mathematics, algorithms, and data structures
Proficient in Python and experienced with ML frameworks (PyTorch preferred, TensorFlow acceptable)
Demonstrated ability to read, understand, and improve upon academic papers
Experience with model architecture design and experimental methodology
Excellent analytical and problem-solving skills, with the ability to translate business requirements into technical solutions
Preferred Qualifications
Experience with modern AI systems: RAG architectures, intelligent agents, LLM orchestration
Experience building and maintaining ML-driven systems in a production environment
Experience with MLOps tools
