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

Tempus AI; New York (NY); On-site; USD 100,000 - 160,000; Full time

Benefits offered by this job

Full range of benefits

Incentive compensation

Stock options

Job summary

Tempus AI is seeking a Machine Learning Scientist specializing in Applied Machine Learning and Agentic AI to work in New York. This role involves developing solutions that automate the discovery of predictive models in oncology and collaborating with various scientific teams.

The ideal candidate holds a PhD or Masters in a relevant field and has strong skills in Python, AI workflows, and communication. A competitive compensation package is offered, reflecting expertise and experience.

Qualifications

• Minimum of a PhD or Masters degree with at least 3 years of relevant experience.
• Quantitative skills in AI agent-based workflows and applied machine learning.
• Proficiency in Python and understanding of complex agentic frameworks.

Responsibilities

• Develop state-of-the-art workflows for agentic AI.
• Integrate oncology data into predictive algorithms.
• Collaborate with clinical teams to define high-value use cases.

Skills

AI agent-based workflows

Python proficiency

LLM Application knowledge

Quantitative and computational skills

Strong communication skills

Education

PhD or Masters with 3+ years experience

Tools

LangGraph (or similar)

Cloud ML deployment

Job description

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

Location: New York, NY

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 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.

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

Gain proficiency in pharmaceutical 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

• Develop complex, state‑of‑the‑art agentic workflows. Build agents capable of 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.

Qualifications

Education and experience: 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).
• Agentic Frameworks: 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.
• 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.
• 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.

Compensation

CHi: $100,000–$150,000
NYC/SF: $120,000–$160,000
The expected salary range above is applicable if the role is performed from California and may vary for other locations (Colorado, Illinois, New York). 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.

Remote Eligibility

For remote roles open to individuals in unincorporated Los Angeles, Tempus reasonably believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: engaging positively with customers and other employees; accessing confidential information, including intellectual property, trade secrets, and protected health information; and appropriately handling such information in accordance with legal and ethical standards. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

EEO Statement

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.