Senior Machine Learning Researcher - Handwriting

GoodNotes Limited; United Kingdom; On-site; GBP 60,000 - 90,000; Full time

Job summary

GoodNotes Limited is seeking a Senior Machine Learning Researcher to lead the development of cutting-edge AI/ML models that enhance handwriting recognition and document analysis. This role offers an opportunity to work in a collaborative and innovative environment, contributing to technologies that empower users worldwide. Join a passionate team dedicated to transforming digital note-taking and pushing the limits of AI capabilities.

Benefits

Meaningful equity in a profitable tech-startup

Budget for home office setup and personal development

Sponsored visits to Hong Kong or London office

Flexible working hours and location

Medical insurance for you and your dependents

Qualifications

• Research track record in Document AI or relevant fields.
• Experience in applying Deep Learning to real-world problems.
• Strong background in software engineering.

Responsibilities

• Research and develop state-of-the-art AI/ML models.
• Collaborate with engineers, QA, and product designers.
• Push boundaries in document analysis and recognition technologies.

Skills

Deep Learning

Handwriting Recognition

Document Layout Analysis

Software Engineering

Python

Tools

PyTorch

TensorFlow

JAX

C++

Rust

Swift

Job description

Senior Machine Learning Researcher - Handwriting

At Goodnotes, we believe that every individual holds untapped potential waiting to be unleashed. By reimagining the way we interact with information, we’re merging human creativity with the breakthrough capabilities of AI. Our renewed vision and mission drive us to create the best medium for human and AI collaboration, empowering users to explore new dimensions of productivity, creativity, and learning. Join us on this journey as we transform digital note-taking into an inspiring and innovative experience.

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 as 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

About the team:

After our huge success with the latest AI releases, 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 a cross-functional engineering team, doing state-of-the-art research that transforms into real-world products, empowering millions of users in their study/work flows. We are a globally-distributed team spanning across Europe and Asia. Thanks to the asynchronous working culture Goodnotes has adopted, time zones will not impact your work-life balance. During the natural overlap of hours within the team, you will have regular meetings to coordinate work between members on the team.

About the role:

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

• Research and develop state-of-the-art AI/ML models to serve millions of users.
• Push the boundaries of document analysis and recognition technologies, such as Handwriting Recognition, Handwriting Synthesis, Stroke Classification and Document Layout Analysis.
• Collaborate closely with a multidisciplinary team, including engineers, QA, and product designers, in a fast-paced environment to deliver features rapidly.

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

• Strong foundation in Deep Learning and sequence modelling, with experience applying them to real-world problems.
• Deep knowledge in Handwriting Recognition, Handwriting Synthesis, Document Layout Analysis and/or related areas.
• Research track record via publications and/or open-source contributions in Document AI or relevant fields.
• Strong grasp of computer science fundamentals with a robust background in software engineering.
• Proficiency in Python and at least one Machine Learning framework such as PyTorch, TensorFlow and JAX.
• Working knowledge of C++, Rust or Swift is a plus.
• Knowledge of model optimization for on-device deployment.
• Excellent communication skills in English.

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

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

By submitting your application, you acknowledge that you have read and understood our Candidate Privacy Notice, which provides important information about the data we collect during the application process. You can find it here .

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