- Location
- San Francisco, Remote, Hybrid
- Workplace
- Hybrid, Remote
- Type
- Full-time
- Department
- Engineering
- Education
- PhD
- Source
- RecruiterFlow
Description
Responsibilities
- Working as part of our team researching and develop machine learning models
- Architecting ML training, validation and inference pipelines
- Designing and implementing approaches to maximizing the potential of data in AI models
- Defining creative solutions to deep problems, and communicating your ideas to the team
Requirements
- PhD or masters in a relevant field of study
- Strong ML research and engineering utilizing established and emerging NLP technologies in a healthcare environment
- Data-centric AI
- Deep learning, problem solving and first principles understanding with proven application
- Experience with information extraction from unstructured and structured healthcare data
- Experience with MLOps
Desirable:
- Experience with deep reinforcement learning
- Experience with knowledge graphs, knowledge bases and ontologies
- Experience with probabilistic graphical models
- Experience with semantic search architectures
Some of the tools we are (and you will be) working with:
- Python
- Pytorch
- SPARQL, Gremlin, NoSQL and SQL
- Docker
- AWS
- Kubeflow
Skills
PythonAWSDockerSQLMachine LearningDeep LearningNLPPyTorch