Senior Software Engineer - Machine Learning, LLM

Our Values:

Dream big —Be visionary strategic and open to innovation Build great things —Work in service of our users always improving and pushing higher Take ownership —Take responsibility with bold decision-making and bias for action Win like a sports team —Be trusting and collaborative while empowering others Learn and grow fast —Never stop learning and iterate fast Share our passion —Share ideas and practice enthusiasm and joy Be user obsessed —Empathetic inquisitive practical

About the team:

After our huge success with handwriting recognition in multiple languages we are accelerating the research and development of cutting-edge features leveraging AI to create the best learning and note-taking platform. You will be part of the cross-functional engineering team turning state-of-the-art research into a real product that impacts the lives of millions of users. They’re a very international team with your future coworkers being based in 6 different countries across Europe and Asia. However due to the asynchronous nature of working that Goodnotes has adopted any time difference will not impact your work-life balance. During the natural overlap of hours within the team you will collaborate with designers ML and software engineers QAs to review any blockers.

About the role:

This is the role for you if you’re excited to work on any of the things listed below:

• Developing and scaling machine learning applications to tens of millions of users

• Building conversational RAG systems for question answering summarization and self-learning over handwritten and PDF documents

• Building a novel platform using GenAI to radically transform how people study and work

• Fine-tuning and prompting large language models to build an AI-first user experience GoodNotes

• Working in a fast-paced multidisciplinary squad with engineers QA product designers to rapidly ship features

The skills you will need to be successful in the above:

• Hands-on experience in building and deploying machine learning systems at scale in production

• Strong experience with RAG systems and agentic workflows with LLMs preferably with some experience with multimodal LLMs

• Strong understanding of computer science fundamentals and a solid background in software engineering

• Experience with any of the following software ecosystems: vector databases (eg. Pinecone Milvus) ML platforms (eg. Metaflow ClearML) cloud ML providers (AWS / Azure GCP) LLM frameworks (eg. LangChain/LlamaIndex) HuggingFace CoreML

• Deep knowledge in one or more of the following ML subfields: classical and neural information retrieval vector search question answering transformer-based language modeling

• Mastery in Python and at least one of the following programming languages: Java/Swift/C++

• Interested in advancing innovation in education

Even if you don’t meet all the criteria listed above we would still love to hear from you! Goodnotes places a lot of value on learning and development and will support your growth if needed.

The interview process:

• An introductory call with someone from our talent acquisition team. They want to hear more about your background what you are looking for and why you’d like to join Goodnotes

• A short Algo/Data structure interview with an Engineer

• An ML technical interview with one of our ML engineers. This is where you get to see what it would be like working at Goodnotes as well as the chance to ask any questions you may have about our ML R&D

• A call with your hiring manager. This is the person who will be managing you day to day working on your growth and development with you as well as supporting you throughout your career at Goodnotes

• Values interview to align with the company culture with a few team members of the team you would be joining or a member of the leadership team.

What’s in it for you:

• Meaningful equity in a profitable tech-startup

• Budget for things like noise-cancelling headphones setting up your home office personal development professional training and health & wellness

• Sponsored visits to our Hong Kong or London office every 2 years and yearly offsite

• Company-wide annual offsite

• Flexible working hours and location

• Medical insurance for you and your dependents