Machine Learning Scientist, Applied Machine Learning and Agentic AI, Pharma R&D

Tempus AI•New York City, NY

1d•Onsite

About The Position

Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. The Machine Learning Scientist, Applied Machine Learning and Agentic AI will contribute to the technical development of cutting-edge agentic frameworks designed to automate the discovery of novel prognostic and predictive models in oncology. This role sits at the intersection of advanced Large Language Model (LLM) orchestration and computational biology. You will be responsible for building and refining "deep agents" capable of hypothesis generation, experimental design, and multimodal ML modeling utilizing foundation models. In this role, you will be a key technical contributor, working closely with senior scientists and engineers to implement system designs and ensure code quality. You will apply advanced scientific methodologies to develop new predictive models and utilize causal inference frameworks to analyze vast multimodal oncology data, helping to scale scientific discovery from a manual process to a high-throughput, automated engine.

Requirements

Minimum: PhD (or Masters degree with 3+ years of relevant experience).
Combining: Quantitative and computational skills, specifically in AI agent based workflows (e.g. Applied Machine Learning, Generative AI, Mathematics, biostatistics).
Biological, medical, or drug development knowledge and data (e.g. oncology, RWE, medical science, or clinical drug development).
Agentic Frameworks: Proficiency in Python and orchestration frameworks, specifically LangGraph (strongly preferred) or similar.
Experience building deep agents with complex state management and graphs.
LLM Application: Deep knowledge of prompt engineering, RAG (Retrieval-Augmented Generation), function calling, and evaluating non-deterministic LLM outputs.
Machine Learning: Strong foundation in survival analysis (CoxPH, RSF) and evaluation metrics for oncology models.
Software Engineering: Adherence to software best practices (unit testing, git) and experience designing scalable systems.
Experience working with clinical trial or real-world data, clinical guidelines (e.g., NCCN for oncology) and emerging RWE methodologies
Track record of success: proven in peer reviewed publications or other proven impact.
Communication Skills: Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences.
Motivated: Thrive in a fast-paced environment and willing to shift priorities seamlessly.

Nice To Haves

Experience in integrative modeling of multi-modal clinical and omics data, preferably with multimodal embeddings and foundation models.
Strong understanding of data and artificial intelligence in Oncology.
Understanding of cancer biology and clinical data.
Experience with deploying ML models in cloud environments.

Responsibilities

Develop complex, state-of-the-art agentic workflows.
Build agents capable of long-horizon planning, tool use and "co-scientist" reasoning.
Leverage oncology foundation models to integrate DNA, RNA, H&E, and clinical data into predictive algorithms.
Collaborate with clinical scientists and pharma partners to define high-value use cases, such as clinical trial design support and treatment de-escalation.