Job Summary
Axon is seeking a Senior CVML Research Scientist to join our team in Finland. This role focuses on advancing computer vision and machine learning research to solve real-world problems and to translate cutting-edge research into practical applications.
Responsibilities
- Lead end-to-end CV/ML research projects from problem framing to deployment
- Develop and evaluate novel algorithms for computer vision and video analysis
- Build, train, and validate models using Python and ML frameworks (e.g., PyTorch, TensorFlow)
- Design rigorous experiments, analyze results, and communicate insights to cross-functional teams
- Collaborate with product, engineering, and data teams to translate research into production features
- Publish findings in internal channels and external venues; contribute to patents or knowledge sharing
- Mentor junior researchers and promote reproducible research practices
- Stay up-to-date with state-of-the-art CV/ML techniques and contribute to roadmap discussions
Qualifications
- PhD in Computer Science, Electrical Engineering, Robotics, or equivalent CV/ML research experience (or MS with a strong research track)
- Strong publications or demonstrated impact in computer vision and machine learning
- Proficiency with Python and ML frameworks (PyTorch, TensorFlow)
- Experience with computer vision, video processing, and data pipelines
- Strong mathematical foundation (statistics, optimization) and problem-solving skills
- Excellent communication and collaboration abilities; fluent in English
About Axon
Axon is a leading technology company focused on innovative hardware and software solutions. We value curiosity, rigorous research, and cross-functional collaboration to drive meaningful product outcomes.
Benefits
- Competitive compensation and comprehensive benefits
- Opportunities for professional growth and collaboration with world-class researchers
- Supportive environment for innovative CV/ML work
Location
Finland
Additional Information
This is a full-time, on-site position located in Finland with potential for hybrid arrangements depending on team needs.