Pitch's goal is to make it as seamless and easy as possible to go from an initial idea to a polished, share-worthy presentation. We will continue to ship design features and stunning templates in service of this goal, but also want to expand our horizon by exploring how artificial intelligence (AI) can augment our users' experiences and make Pitch the fastest content creation product on the market.
As Research Data Scientist, you'll join a small, focused group of engineers to create and extend AI methods for the core product area of document creation and design. You'll be exploring state-of-the-art methods in research and the industry to evaluate and customise them for the use cases at Pitch. You'll develop completely novel approaches that allow us to unlock innovation and business value. Through collaboration with engineers and designers, you'll ultimately put these approaches into practice and get your models into production. Sounds interesting? Read on for more details.
What you'll get to do...
Develop custom solutions for use-cases in automation and augmentation for document creation, including those that aren't covered by commodity and textbook AI solutions.
Customize existing methods to create tailored AI solutions aiming for the best possible UX.
Build our in-house expertise in AI methods to supplement existing engineering and product expertise.
Provide analyses and foundational proof of concept implementations, which can be productized collaboratively with the other team members.
Contribute to AI at Pitch from the beginning and help us to refine our vision and strategy.
Expand our and your own horizon about what is possible with AI in presentation design.
Who you are...
You have a broad Machine Learning and AI experience and skillset, e.g. deep learning, explainable models, self- & unsupervised methods, NLP, CV, generative methods.
In-depth knowledge about AI approaches, how to assess them and their feasibility as well as tradeoffs for a given use case.
Ability to communicate research results to stakeholders.
Experience with Python and the Python Data Science ecosystem.
Deep understanding of statistics, linear algebra, calculus, algorithms & computational complexity.
Structured problem-solving ability and a willingness to learn and adapt.
Experience with developing and improving models that are deployed to production and have user facing impact.
You strive for excellent models, while also considering the needs to deploy and operate them.
Bonus points for...
Knowledge about document layout and design, ideally in the context of AI and automation.
Experience with creation and execution of research plans based on input from team members and product management.
Experience with Data Science workflows and reproducible research in tech companies.
Software engineering practices like version control, scripting, modularisation, testing, containers and virtual environments.
Experience and eagerness to explore and work with methods from symbolic AI where appropriate.
Prior experience with Clojure or another functional programming language.
We value diversity of perspective and seek to build an inclusive workplace that welcomes people from all different backgrounds.