The company is looking to implement advanced AI models across clinical and genomics applications to drive real-world results in precision medicine.
Requirements
- Proficiency in deep learning frameworks such as PyTorch, TensorFlow, or Keras
- Experience with integrating LLMs into downstream clinical workflows
- Expertise in fine-tuning and optimizing pre-trained models for domain-specific applications
- Familiarity with agent frameworks and retrieval-augmented generation techniques
- Experience with semantic search, hybrid search, and vector databases
- Strong programming skills in Python
- Experience with containerization and orchestration
Responsibilities
- Identify and evaluate best-fit models for a wide range of tasks
- Fine-tune or optimize foundation models for targeted clinical/genomic outcomes
- Design for scalability, observability, and maintainability in ML deployments
- Package and serve models for production inference
- Build agentic systems that can answer complex clinical or genomic queries
- Automate human-in-the-loop workflows for expert-level decision support
- Own the ML engineering stack, from model integration and experimentation to cloud-native deployment
Other
- PhD in Computer Science, Computational Biology, Bioinformatics, Statistics, or related field, or equivalent practical experience
- 8+ years of experience applying machine learning to real-world problems
- Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents
