Overview In this role you advance Tempus's platform for pharmaceutical R&D by developing cutting-edge ML and generative AI solutions. You collaborate with cross-functional teams to apply causal models, LLMs, and agent-based tools to real-world and molecular data. Your work shapes drug development strategies and brings impactful insights to pharma partners. You will lead scientific discussions, mentor colleagues, and stay at the forefront of AI in life sciences to accelerate patient-centered solutions.
Responsibilities- Develop algorithms, causal models, and agent-based tools to enhance the Tempus platform for drug R&D
- Drive continual ML and generative AI innovations aligned with client needs and industry trends
- Collaborate with Research, Engineering & Data Science teams to deliver novel computational solutions
- Co-develop solutions with pharma partner science and clinical teams
- Communicate complex results to diverse external stakeholders and train internal/external audiences
- Provide AI guidance and hands-on coaching to computational biologists and RWE scientists
- Stay current with industry trends to revolutionize drug R&D
Key requirements- PhD or Masters with 2+ years of relevant experience, plus 2+ years in industry/post-doc
- Strong background in causal AI, causal inference, and/or explainable AI
- Biology/medicine knowledge with data in oncology, RWE, or clinical drug development
- Proficiency in R, Python, and SQL
- Experience with agentic orchestration frameworks (e.g., LangChain, LangGraph, AutoGen, DSPy)
- Hands-on experience with causal methods (DAGs, counterfactuals, heterogeneous treatment effects)
- Familiarity with LLM-driven agent architectures, prompt engineering, RAG, and function calling
- Experience with molecular data, clinical trial or real-world data; publications in peer-reviewed venues
- Excellent written and verbal communication; client-facing and training experience
- Thrives in fast-paced environments and can shift priorities
- excellent communication
- client-facing ability
- ability to explain complex concepts clearly
- causal AI / causal inference
- generative AI
- LLMs and agent architectures
