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

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. 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.

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

Location:

New York, NY

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.

Description

Data Expertise:
Tempus has one of the largest multimodal patient datasets ever collected, providing a unique opportunity to work with extensive and diverse data. Become an expert in Tempus' vast epidemiological, clinical, genomic, transcriptomic and pathology imaging data, along with the latest tools and techniques for their analysis and modeling.

Teamwork and collaboration:

Work with Research, Engineering & Data Science teams across Tempus' expansive data science community to develop and deliver innovative computational solutions.

Co-develop solutions with Pharma partner science and clinical teams

Drug R&D Expertise:
Work with leading pharmaceutical companies. Gain proficiency in their strategies, drug modalities, and pipelines to identify where the Tempus platform can add value.

Scientific Communication:
Skillfully navigate client interactions to extract and communicate the most impactful insights driving new R&D opportunities; effectively communicate complex technical results and methodologies to diverse external stakeholders.

Personal development:
Continuously immerse yourself in the latest industry trends, best practices, and advancements in machine learning and AI to revolutionize drug R&D

Responsibilities

Agentic AI:
Develop complex, state-of-the-art agentic workflows. Build agents capable of long-horizon planning, tool use and "co-scientist" reasoning.

Multimodal Modeling:
Leverage oncology foundation models to integrate DNA, RNA, H&E, and clinical data into predictive algorithms.

Scientific

Innovation: Collaborate with clinical scientists and pharma partners to define high-value use cases, such as clinical trial design support and treatment de-escalation.

Qualifications

Education and experience:

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).

Technical/Scientific

Skills:

Agentic Frameworks:
Proficiency in Python and orchestration frameworks, specifically Lang Graph (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.

Preferred Skillsets/Background

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…