- Remote (US)
About the Role
- Help develop state-of-the-art abstractive text summarization systems using large language models (LLMs)
- Manage data ingestion processes, oversee labeling efforts and monitor data quality to support your own machine learning models.
- Proactively identify model failure cases and identify new approaches for improving our machine learning models.
- Continued education about the latest machine learning algorithms, techniques, and technologies.
- 5+ years of professional experience as an ML Engineer (2+ with PhD)
- Professional or extensive academic experience with text summarization
- BS & MS in Computer Science or related field, PhD preferred
- Ability to leverage large-scale compute resources, including distributed GPUs, to train and evaluate very large neural networks.
- Familiarity with advanced models such as BERT
- Working knowledge of machine learning techniques and algorithms, ranging from logistic regression to deep neural networks.
- Professional experience with basic NLP, including techniques such as tokenization, lemmatization, and TF-IDF vectorization.
- Ability to manage datasets and evaluate ML model performance, using train/test splits, cross-validation, and task-specific metrics.
- Proficiency in Python, including basic skills such as setting up virtual environments, installing packages from PyPI, and debugging common problems using online resources.
- Ability to create large software projects that are organized, well-structured, thoughtfully documented, and automatically tested.
- Experience deploying machine learning models to production.
Nice to Have
- Proven record of working with open-source communities.
- Familiarity with web frameworks such as Flask, Tornado, Starlette, or FastAPI.
Salary Range or On Target Earnings:
Job information can change without notice
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