Senior Scientist: Applied ML & Generative AI for Drug R&D

Tempus AI; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

Tempus AI is seeking a Senior Scientist, Applied Machine Learning and Generative AI, Pharma R&D to perform complex analyses and develop algorithms, causal models, and agent-based tools to advance the Tempus platform for drug R&D. You will apply causal AI, generative AI and LLMs to real-world biomedical data, communicating findings to stakeholders.

The role requires strong ML expertise, experience with agent architectures, prompt engineering and RAG, and the ability to collaborate across R&D

Qualifications

• PhD or Masters with 2+ years of relevant experience.
• Plus an additional 2+ years of industry/post-doc experience.

Responsibilities

• Data Expertise: work with Tempus multimodal datasets to model epidemiology, clinical, genomic, and imaging data.
• Innovation: drive ML and generative AI capabilities to meet client needs and industry trends.
• Teamwork and collaboration: collaborate with Research, Engineering & Data Science teams and Pharma partner science/clinical teams.
• Drug R&D Expertise: learn pharmaceutical strategies, modalities, and pipelines to identify Tempus platform value.
• Scientific Communication: communicate complex results to diverse external stakeholders clearly.

Skills

Causal AI

Causal Inference

Explainable AI

R

Python

SQL

LangChain

LangGraph

AutoGen

DSPy

Prompt Engineering

RAG

Tool Use

Communication

Education

PhD or Masters with 2+ years

Tools

LangChain

LangGraph

AutoGen

DSPy

Job description

Tempus AI is seeking a Senior Scientist, Applied Machine Learning and Generative AI, Pharma R&D to perform complex analyses and develop algorithms, causal models, and agent-based tools to advance the Tempus platform for drug R&D. You will apply causal AI, generative AI and LLMs to real-world biomedical data, communicating findings to stakeholders.

The role requires strong ML expertise, experience with agent architectures, prompt engineering and RAG, and the ability to collaborate across R&D