Staff/Lead Machine Learning Engineer, Mimica

• A researcher mindset, with curiosity and rigour in exploring and solving complex problems
• Experience with deep learning methods and techniques
• Experience with transformer and embedding architecture
• Strong technical skills in designing, setting up, running, and evaluating experiments
• Proficiency in supervised and unsupervised learning techniques
• Excellent written communication skills, including the ability to produce clear and concise reports
• Strong Python programming skills, emphasising clear, readable code; while productionization support may be involved, it is not the primary focus
• A drive to continually develop your skills, improve team processes, and reduce technical debt
• Fluency in English, with the ability to effectively communicate abstract ideas, complex concepts, and trade-offs

Desirable

• In this role, you will be a member of the ML Chapter and work with the Mapper team. Mimica Mapper is one of our main products that creates intuitive flowcharts that map out user and team workflows
• Its architecture includes components designed to automatically detect task similarities
• For the first 3 to 6 months, you will own projects to improve the task similarity algorithm and the use of the Mapper
• Design and run experiments to improve our task-similarity algorithms, using a mix of classic and deep learning techniques
• Write clear technical reports that document experiments and their results
• Write clean, readable, and maintainable Python code, assuring best practices
• Interface with our internal Process Analyst team to discover opportunities on which parts of the product can be automated, find out pain points and explore automation solutions by leveraging ML
• Support productionization (although we have a dedicated MLOps Engineer for that!)
• Actively collaborate and engage in technical discussions with the other Engineers, Product Managers in the team and ML Chapter, to drive the development of the product
• Contribute to knowledge sharing and the improvement of our processes