At Pitch, we believe that the problem space of modern presentation software is ripe for innovation, and calls for Artificial Intelligence (AI) and Machine Learning (ML) to augment the many redundant and mundane user-tasks required to create a delightful and engaging presentation.
As a Machine Learning Engineer at Pitch, you will join a small, focused group of engineers to create and extend AI methods for the core product area of document creation and design. You will participate in the development of completely novel approaches that allow us to unlock innovation and business value.
If you are passionate about applying your Machine Learning Engineering skills to Pitch's uniquely rich dataset (image data, text, layout and beyond) and pioneer state of the art techniques in the presentation/design tool domain, we'd love to hear from you!
Curious about our engineering culture? Our CTO and co-founder Adam Renklint recently contributed to a blog post that dives deep into our technical foundation, our iterative approach to development and our philosophy on building a sustainable engineering culture that’s all about joyful work.
What you will do
- Contribute to refining Pitch's AI Vision and Strategy by expanding our and your own horizon with regards to what is possible with AI in presentation design.
- Extend the teams' ML and data engineering capacities, allowing us to identify further machine learning opportunities across Pitch.
- Support the team by making Pitch data available to AI use cases in a robust, safe and scalable manner, and by providing a variety of tools to make reproducible experiments seamless.
- Actively contribute to Research to leverage a variety of ML approaches to build AI features, capabilities and services for other teams to build upon.
- Work on the implementation and translation of ML models into delightful user features.
- Scope and design the infrastructure needs for deploying our models into production.
- Ensure the production-readiness of our ML systems, and once deployed, make sure models are running as expected (e.g. evaluation & monitoring).
What you should offer
- At least 3-4 years' experience in Software Engineering, with a strong understanding of Machine Learning needs and requirements.
- Previous experience in a SaaS company and/or working on a design or document-editing product would be ideal
- Good understanding of the ML models lifecycle from data analysis to production grade code and operating models.
- Experience with Data Science workflows and reproducible research in tech companies.
- Previous experience or knowledge in establishing ML architecture and best practices.
- Experience with MLOps and data engineering such as ETL, pipeline orchestration and data lineage.
- Experience with Python and the Python Data Science ecosystem.
- Knowledge of SQL and data warehouse technologies.
- Familiarity of software engineering practices such as version control, scripting, modularisation, testing, containers and virtual environments.
- Structured problem-solving ability and a willingness to learn and adapt.
- Proactive communication and collaboration with peers, internal stakeholders and other teams
It would be nice if you had
- Broad Machine Learning and AI experience and skillset, e.g. deep learning, explainable models, self- & unsupervised methods, NLP, CV, generative methods.
- Experience with developing and improving models that are deployed to production and have user facing impact.
- Knowledge about AI approaches, how to assess them and their feasibility as well as tradeoffs for a given use case.
- Prior experience with Clojure or another functional programming language.
- AWS experience: RDS, CloudFormation, IAM, Lambda, SageMaker, etc.
- Enthusiasm for sharing knowledge internally and externally