Senior AI Engineer, LLM

We want to make work and study more efficient and enjoyable, by providing the best digital paper solution possible. We plan to be the go-to tool for all forms of notes. Our digital paper and learning ecosystem inspires anyone to take notes, share what they know, collaborate with others, and learn as a community

We want to make work and study more efficient and enjoyable, by providing the best digital paper solution possible. We plan to be the go-to tool for all forms of notes. Our digital paper and learning ecosystem inspires anyone to take notes, share what they know, collaborate with others, and learn as a community

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 based in 6 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 agentic and multimodal AI systems to tens of millions of users
• Developing and scaling an advanced RAG system for question answering, summarization, self-learning, and AI coediting over handwritten and PDF documents
• Building a novel medium combining multimodal LLMs and freeform notetaking technology to radically transform how people engage with their information
• Productionizing LLM pipelines 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 AI applications at scale in production
• In-depth LLM experience with either RAG or agentic workflows
• Good understanding of computer science fundamentals
• Mastery in Python and at least one additional programming languages for full-stack engineering (eg. Java/Swift/C++)
• Solid experience with most of the following software ecosystems and technologies in full-stack engineering:
• Cloud platforms and serverless computing (e.g., AWS Lambda/ECS/SageMaker, Google Cloud Run/Cloud Functions)
• Containerization and orchestration (eg. Docker, Kubernetes)
• Vector databases (e.g., Pinecone, Milvus, Weaviate)
• ML experimentation and model management platforms (e.g., MLflow, Weights & Biases, ClearML)
• LLM providers and AI services (eg. AWS Bedrock, Azure OpenAI Service, Google Vertex AI)
• Open-source AI community platforms and frameworks (eg. HuggingFace, LangChain, LlamaIndex)
• API development frameworks (eg. Python FastAPI, Flask)
• Openness to work across API logic level and infrastructure level
  

Skills that would help but not required:

• Experience building applications with multimodal LLMs
• Deep understanding of transformer models and their applications in information retrieval, question answering, and document summarization
  
  

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
• 3 60-minute Technical interviews:
• A short Algo/Data structure interview with an Engineer
• An exploration of your work experience with AI. 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 technical interview on NLP and LLMs
• 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
• A 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
  
  

Note: Employment is contingent upon successful completion of background checks, including verification of employment, education, and criminal records.

Note: Employment is contingent upon successful completion of background checks, including verification of employment, education, and criminal records.