Debugging, Distributed systems, ETL/ELT technologies, Large scale data processing, MLOps tools, Optimization, TensorFlow, TorchServe
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
As our MLOps engineer, you will be responsible for building our model inference and training infrastructure for both internal and external users. This includes model serving using Tensorflow Serving or TorchServe; performance optimization; monitoring, maintenance, and reporting; integration with labeling and data curation processes; development of generic training and inference services; as well as debugging and troubleshooting.
About You
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.
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.
San Francisco, CA, USA
8-10 year
San Francisco, CA, USA
6-8 year