Design and build ML pipelines for production, managing the entire ML lifecycle. Collaborate with teams to improve data handling and promote best practices.
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 like an owner
- 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 role:- 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
- 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
Help us make GoodNotes smarter thanks to a fast and robust ML Lifecycle. You will have the entire support of the organization to use the latest technologies to design the process tools and operations for Model production, training, and delivery. You will work with passionate teammates who are experts in the latest ML technologies and love sharing it.
This is the role for you, if you’re excited to work on the things listed below:- Unify our ML system development and operations to accelerate the iteration speed of building ML models
- Design and build a robust pipeline that manages the entire ML lifecycle (i.e. developing, packaging, testing, deploying, monitoring and training models)
- Strong interest in ML and the latest ML engineering practices, including LLM/ RAG applications.
- Suggest new ideas to tackle edge cases ranging from data collection to model development
- Collaborate with ML, Data and QA teams to identify opportunities to improve our handling on both structure and unstructured data (e.g. images, text, videos etc.)
- Promote best practices for managing the ML lifecycle within the team
The skills you will need to be successful in the above:
- Experience building reliable ML pipelines in production that manage the ML lifecycle on AWS and/ or other cloud providers
- Understand the requirements of a well-managed ML lifecycle such as having model and data versioning, experiment tracking, feature store, CI/CD, and continuous training (CT) on NVIDIA GPUs.
- Mastery of Python and at least one programming language such as Java, Kotlin, Scala, Golang, Rust, C++ etc.
- Mastery in model serving practices for batch and stream processing.
- Hands-on experience building and operation for data lake using one or more of the following big data frameworks or services: Spark, Kafka, Airflow, DBT, Debezium, AWS Athena, AWS Glue, Delta lake/ Iceberg etc.
- Experience with Kubernetes, Docker, Terraform or other cluster management solutions on AWS or other cloud providers
- Deep understanding of computer science fundamentals and a solid background in software engineering
- Basic knowledge of building ML models
- Basic knowledge of model optimization, and model export to multiple formats.
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.
- A job-related technical take-home challenge, for which you are allowed to use AI to assist you in completing it
- 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 technical interview call with one of our engineers. This is where you get to see what it would be like working at Goodnotes as well as the chance to ask any engineering questions you may have
- A one-hour behavioural interview 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 support you throughout your career at Goodnotes. Based on seniority you might also need to interview with our VPE.
- A one-hour call with 2-3 stakeholders you’d work closely with
- 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
- Company-wide annual offsite
- Flexible working hours and location (EU timezone only)
- 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.
Top Skills
Airflow
AWS
Aws Athena
Aws Glue
Dbt
Debezium
Delta Lake
Docker
Iceberg
Kafka
Kubernetes
Python
Spark
Terraform
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