Senior Scientist II, Applied Machine Learning and Translational Agentic AI, Life Science 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. The Senior Scientist II, Applied Machine Learning and Translational Agentic AI, Life Science R&D will lead the scientific 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 translational science. 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 design, code quality, and the strategic roadmap for agentic capabilities. Beyond technical 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 and translational science: 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 : 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, translational or drug development knowledge and data (e.g. oncology, RWE, medical science, or clinical drug development). Technical/Scientific Skills: Agentic Frame ... (truncated, view full listing at source)