AI Research Manager

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 Operate like an owner —Propel company success and impact with an entrepreneurial mindset 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 delivering a number of agentic and other generative AI experiences to help our users automate mundane tasks in their workflows, we are accelerating the research and development of cutting-edge features leveraging AI to create the best productivity and note-taking platform. You will be part of the applied research team, delivering state-of-the-art multi-modal LLMs to our product teams for features that impacts the lives of millions of users. They’re a very international team, with your future coworkers based in 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 other researchers, as well as designers, software engineers and QAs to review any blockers.

About the role:

As the LLM Research Manager, you will lead a team of LLM and multimodal researchers building the AI systems that power Goodnotes’ most advanced user experiences. You will combine strong technical depth with people leadership, research direction, and cross-functional execution. This is the role for you if you're excited to:

• Lead research direction for agentic and multimodal systems enabling freeform and spatial (visual + relational) content understanding

• Guide the design and scaling of advanced RAG systems across handwritten notes, whiteboards, typed documents, and PDFs

• Shape a new medium that combines multimodal LLMs with freeform note-taking to transform how people think, create, and engage with information

• Mentor and grow a team of researchers through goal setting, research rigor, experimentation methodology, and career development

• Partner with engineering to productionize LLM pipelines and ensure high-quality, reliable deployment of research outputs into user-facing features

• Drive execution in a fast-paced, multidisciplinary environment, aligning research priorities with product goals and shipping impactful AI capabilities rapidly

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

• 8+ years of relevant industry experience, including 2+ years leading research teams in LLMs, multimodal models, or related areas

• Strong track record driving LLM/VLM research from ideation to production, with a particular focus on post-training (SFT, RLHF/GRPO, DPO/IPO, preference modeling)

Technical Depth

• Deep theoretical understanding of modern ML (representation learning, optimization, regularization, curriculum/data selection strategies)

• Proven experience with non-text modalities such as handwriting/ink, sketches, diagrams, layouts/UI, or canvas/whiteboard content

• Strong hands-on experience with Python and a major deep learning framework, with comfort reviewing/steering complex model code and experiments

• Experience building or overseeing evaluation pipelines for LLMs/VLMs, including custom datasets, metrics, and human evaluation processes

• Expertise in multimodal or structured architectures (vision encoders, layout-aware models, graph-based representations, hybrid tokenization)

Execution & Collaboration

• Demonstrated ability to design principled experiments, diagnose failure modes, and guide systematic iteration across a research team

• Strong engineering hygiene: reproducibility, experiment tracking (MLflow or similar), versioning, documentation, and reliable model handoff to engineering

• Excellent communication skills—able to translate complex modeling trade-offs to product stakeholders and provide clear technical direction to researchers

• Nice to have: research publications, open-source contributions, or industry impact in LLMs/VLMs; prior work on freeform or spatial content is a major plus

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

• Two 60-minute Technical interviews:

  • An exploration of your previous project experiences with AI and an open-ended problem where you will be able to showcase your system thinking and problem-solving skills. 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

  • An in-depth technical interview on NLP and LLMs

• Hiring Manager interview focused on leadership, team direction, and how you guide researchers through complex frontier work

• 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, Singapore 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.