Translational Scientist, Applied Machine Learning, Agentic AI

Tempus AI

Translational Scientist developing and refining AI frameworks for oncology at Tempus. Collaborating with Pharma partners and utilizing multimodal data for R&D insights.

Posted 6/5/2026full-timeNew York City • Illinois, New York • 🇺🇸 United StatesMid-LevelSenior💰 $100,000 - $150,000 per yearWebsite

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Contribute to the technical development of cutting-edge agentic frameworks designed to automate the discovery of novel prognostic and predictive models in oncology
Responsible for building and refining "deep agents" capable of hypothesis generation, experimental design, and multimodal ML modeling utilizing foundation models
Key technical contributor, working closely with senior scientists and engineers to implement system designs and ensure code quality
Apply advanced scientific methodologies to develop new predictive models and utilize causal inference frameworks to analyze vast multimodal oncology data
Collaborate with Research, Engineering & Data Science teams across Tempus’ expansive data science community to develop and deliver innovative computational solutions
Work with leading pharmaceutical companies to identify where the Tempus platform can add value
Skillfully navigate client interactions to extract and communicate impactful insights driving new R&D opportunities

Requirements

What you’ll need

Minimum: PhD (or Masters degree with 3+ years of relevant experience)
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)
Proficiency in Python and orchestration frameworks, specifically LangGraph (strongly preferred) or similar
Experience building deep agents with complex state management and graphs
Deep knowledge of prompt engineering, RAG (Retrieval-Augmented Generation), function calling, and evaluating non-deterministic LLM outputs
Strong foundation in survival analysis (CoxPH, RSF) and evaluation metrics for oncology models
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.
Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences.
Thrive in a fast-paced environment and willing to shift priorities seamlessly.

Benefits

Comp & perks

Incentive compensation
Restricted stock units
Medical and other benefits depending on the position

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonAI agent based workflowsApplied Machine LearningGenerative AIMathematicsbiostatisticsdeep agentsprompt engineeringsurvival analysisevaluation metrics

Soft Skills

communication skillscollaborationclient interactionadaptabilityproblem-solving

Certifications

PhDMasters degree