What we are building
Mimica's mission is to empower enterprises, teams, and individuals to reclaim their most precious resource — time and work more efficiently, with greater purpose and impact. Our AI-powered task mining observes employee actions across the desktop and categorizes them into detailed process maps. Mimica’s process intelligence highlights inefficiencies, prioritizes improvements based on ROI, recommends the optimal technology for automation (RPA, intelligent document processing, GenAI), and provides a blueprint for building new automations and transforming work.
Our approach to engineering
- We prioritize customer needs first
- We work in small, project-based teams
- We have flexibility in terms of the problems we work on
- We own the full lifecycle of our projects
- We avoid silos and encourage taking up tasks in new areas
- We balance quality and velocity
- We have a shared responsibility for our production code
- We each set our own routine to maximize our productivity
What will you own
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 general use of the Mapper and labelling process. We're a startup, so there will always be the opportunity to work with other products and projects.
Part of your day-to-day
- 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 data-labellers 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.
💰 Generous compensation + stock options - aligned with our internal framework, market data, and individual skills. 🏢 Distributed work: Work from anywhere - fully remote, in our hubs, or a mix. 💻 Company-issued laptop*, remote setup stipend, and co-working budget 📍 Flexible schedules and location ☀️ Ample paid time off, in addition to local public holidays 🍼 Enhanced parental leave 🧘♀️ Health & retirement benefits 📖 Annual learning & development budget - up to £500 / €600 / $650 per year 🌴 Annual workaways and regular virtual & in-person socials 🌍 Opportunity to contribute to groundbreaking projects that shape the future of work
- 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
Bonus
- Graph ML knowledge
- Experience working in a startup/scale-up environment
