Responsibilities
- Lead full-cycle machine learning research, spanning hypothesis development, implementation, deployment, and ongoing performance tracking
- Create and test new techniques for aligning large language models with private clinical datasets
- Design and validate strategies to detect model hallucinations, improve attribution, and ensure consistent reliability
- Construct and manage high-integrity datasets, prioritizing robust evaluation frameworks and benchmark accuracy
- Analyze academic research to distinguish effective methods and adapt promising approaches for real-world use
- Define standards for experimental methodology, emphasizing statistical rigor and reproducible results
- Partner with engineering teams to transition models into production, including MLOps, infrastructure, and deployment workflows
- Advance long-context and multimodal modeling techniques that integrate structured and unstructured clinical records
- Provide guidance to fellow researchers and elevate overall research quality across the organization
- Support external visibility by publishing research, presenting at events, and contributing to talent acquisition
Work Arrangement
Remote (Country)
Other
This position requires residency within the United States, as it is fully remote and not available internationally
