Senior Full Stack Engineer, Active Learning
- 5 applicants (11%)
Senior Full Stack Engineer, Active Learning
ENGINEERING ACTIVE LEARNING
Labelbox’s mission is to build the best products to align with artificial intelligence. Real breakthroughs in AI are reliant on the quality of the training data. Labelbox’s data engine enables organizations to dramatically improve the quality of their training data, which makes their machine learning models more accurate and performant. We are determined to build software that is more open, easier-to-use, and singularly focused on helping our customers get to production AI faster.
Current Labelbox customers are transforming industries within insurance, retail, manufacturing/robotics, healthcare, and beyond. Our platform is used by Fortune 500 enterprises including Allstate, Black + Decker, Bayer, Warner Brothers and leading AI-focused companies including FLIR Systems and Caption Health. We are backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures (Google’s AI-focused fund), Databricks Ventures, Snowpoint Ventures and Kleiner Perkins.
About Active Learning
In machine learning, data curation is only part of the battle to create a successful model. Once data is labeled, it is used to train, validate, and test models with the goal of production deployment. Upon validation, an engineer may discover a model may perform well at predicting one particular subset or class of data, but struggle on another.
The mission of the Active Learning team is to enable rapid model iteration with tooling and workflows to surface insights into model performance after completing the training process. We seek to give engineers the tools they need to validate models against ground truth data, spot model inaccuracies, identify gaps in cohorts of training data, and initiate workflows to improve that data for future model versions.
About the Role
The Active Learning Products team owns the end to end user experiences for the core products focused around model development and iteration at Labelbox. This team is responsible for creating elegant, intuitive workflows to empower machine learning engineers to understand and improve model performance through data analysis.
As our frontend engineer, you will create rich experiences for engineers and scientists working on the cutting edge of machine learning who are focused on the iteration phase of model development. You will seek to understand the current model iteration workflows of machine learning engineers, and build rich software experiences to introduce automation and surface valuable insights. You will be expected to utilize the full range of product design, interaction design, and visual design skills to create immersive, intuitive, end-to-end experiences.
- Experience with modern frontend frameworks such as React, Redux, RxJS, or similar frameworks.
- Experience working with large quantities of data and efficiently fetching, storing, and displaying these datasets in ways to create performant interactions.
- Experience with backend development or working with backend API’s built on NodeJS, Apollo GraphQL, Redis, or similar languages and frameworks.
- Experience using HTML5 Canvas-based drawing and image manipulation.
- You are driven to independently deliver impactful product and engineering initiatives and have previously owned major projects from design to implementation.
- You appreciate a fast-paced environment that takes an iterative approach to product development.
Nice to Have
- Experience designing MySQL, PostgreSQL, or equivalent databases and data models.
- Experience working with CI/CD systems, building test and release automation.
- Experience building workflows for data science and machine learning development.
Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for this United States based position is $170,000 – $215,000. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.
Do great work. From anywhere.
We hire great people regardless of where they live. Work wherever you’d like as reliable internet access is our only requirement. We communicate asynchronously, work autonomously, and take ownership of our work.