Translational Scientist, Applied Machine Learning and Agentic AI, Pharma R&D
AI summary of the role
This role develops cutting-edge agentic AI frameworks to automate the discovery of prognostic and predictive models in oncology, working at the intersection of LLM orchestration and computational biology.
No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.
What you’ll do
- Develop complex, state-of-the-art agentic workflows with 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.
- Become an expert in Tempus’ multimodal patient data and apply advanced methodologies to develop new predictive models.
What you’ll bring
- PhD (or Masters with 3+ years experience) in quantitative/computational field with biological/medical knowledge.
- Proficiency in Python and orchestration frameworks, specifically LangGraph or similar.
- Deep knowledge of prompt engineering, RAG, function calling, and evaluating non-deterministic LLM outputs.
- Strong foundation in survival analysis (CoxPH, RSF) and evaluation metrics for oncology models.
Technologies
LangGraph · Python · RAG · survival analysis · CoxPH · RSF · foundation models · multimodal embeddings · LLM orchestration · agentic frameworks
About Tempus
AI-enabled precision medicine platform combining diagnostics, multimodal clinical/molecular data, and analytics to guide patient care and accelerate biopharma R&D.
Public · 2000–5000 people
Source and classification
Internal deployment & tooling · Evidence for this classification:
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,
More from the job description
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 [... source excerpt omitted ...] 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 plannin [... source excerpt omitted ...] 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
Employer postings · Data from · Sources