Senior Scientist II, Applied Machine Learning and Agentic AI, Pharma R&D Full-time

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

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.

The Senior Scientist II, Applied Machine Learning and Agentic AI will lead 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 RWD. You will be responsible for architecting sophisticated “deep agents” capable of hypothesis generation, experimental design, and multimodal ML and AI modeling utilizing foundation models.

As a senior technical contributor, you will act as a force multiplier for the team, taking ownership of system design, code quality, and the strategic roadmap for agentic capabilities. Beyond system architecture, you will innovate on core scientific methodology by developing new predictive models and causal inference frameworks to rigorously analyze vast multimodal oncology data, effectively scaling 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.
• Scientific Leadership & Influence: Empower computational biologists and RWE scientists through targeted AI guidance and hands on coaching to increase AI tool adoption to maximize impact.
• 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 Architecture: Design and build complex, state-of-the-art agentic workflows. Develop 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 5+ years of relevant experience). Plus an additional 2+ years of relevant industry or post-doctoral experience that involves medicine and AI. 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: Expert-level 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.

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 models in cloud environments.

NYC: $115,000-$175,000. The expected salary range above is applicable if the role is performed from New York and may vary for other locations (California, Colorado, Illinois). Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position.

We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.