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Prompt Engineering Biology Jobs (NOW HIRING)

Waters Corporation

Field Service Specialist

Houston, TX · On-site

Establishes and maintains prompt professional communications with customers and internal resources ... Associates Degree in Chemistry, Engineering, Biology, or other science related field * Bachelors ...

Waters Corporation

Field Service Specialist

Houston, TX · On-site

Establishes and maintains prompt professional communications with customers and internal resources ... Associates Degree in Chemistry, Engineering, Biology, or other science related field * Bachelors ...

GenScript/ProBio

AI Researcher Intern

Piscataway, NJ

$30/hr

... biologics development and manufacturing, and cell therapy.GenScript is committed to striving ... Familiar with Prompt Engineering, Function Calling, Tool Use, and related technologies * Experience ...

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How much do prompt engineering biology jobs pay per year?

As of Jun 8, 2026, the average yearly pay for prompt engineering biology in the United States is $62,977.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,000.00 and $72,000.00 per year, depending on experience, location, and employer.

What is the difference between Prompt Engineering Biology vs Data Scientist?

AspectPrompt Engineering BiologyData Scientist
Required CredentialsBiology degree, knowledge of AI promptsStatistics, computer science, or related degree
Work EnvironmentResearch labs, biotech firms, AI companiesTech firms, finance, healthcare, research
Industry UsageBiotech, AI in biology, healthcareMultiple industries including tech, finance, healthcare
Common Search/ComparisonYesYes

Prompt Engineering Biology focuses on designing prompts for AI models in biological contexts, often requiring biology expertise combined with AI knowledge. Data Scientists analyze data to extract insights across various industries. While both roles involve AI and data handling, Prompt Engineering Biology emphasizes biological applications and prompt design, whereas Data Scientists focus on data analysis and modeling across diverse sectors.

How does a Prompt Engineering Biologist typically collaborate with data scientists and software engineers during a project?

Prompt Engineering Biologists often work closely with data scientists to design and refine prompts that optimize biological data analysis using AI models. They collaborate with software engineers to integrate these prompts into automated pipelines and troubleshoot technical challenges. This interdisciplinary teamwork ensures that biological insights are accurately extracted and interpreted, leading to more robust AI-driven research outcomes. Open communication and a strong understanding of both biological concepts and AI capabilities are essential for effective collaboration in this role.

What are the key skills and qualifications needed to thrive as a Prompt Engineering Biologist, and why are they important?

To thrive as a Prompt Engineering Biologist, you need a strong background in biology, computational modeling, and data analysis, usually supported by an advanced degree in a life science or bioinformatics. Familiarity with programming languages (such as Python or R), machine learning frameworks, and bioinformatics tools is typically required. Critical thinking, creativity, and effective interdisciplinary communication help professionals excel in designing biological prompts and interpreting complex results. These skills and qualities are crucial for developing innovative solutions and translating biological data into actionable insights using AI-driven approaches.

What is prompt engineering in biology?

Prompt engineering in biology refers to the process of designing and optimizing prompts or queries for AI models, such as large language models, to generate accurate, relevant, and useful responses for biological research and applications. This can include generating scientific hypotheses, interpreting biological data, summarizing research papers, or assisting in experiment design. Prompt engineers in biology need a strong understanding of both computational techniques and biological concepts to effectively bridge the gap between AI tools and life sciences. Their work enables more efficient research, data analysis, and discovery in the biological sciences.

Senior Scientist II, Applied Machine Learning and Translational Agentic AI, Life Science R&D

Tempus AI

Manhattan, NY • On-site

Full-time

Posted 23 hours ago

Tempus AI rating

6.7

Company rating: 6.7 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

147th of 186 rated software companies

Job description

Job Summary:
Tempus AI is focused on precision medicine and advancing the healthcare industry through AI technology. The Senior Scientist II will lead the development of agentic frameworks for automating the discovery of predictive models in oncology, collaborating with various teams to innovate on scientific methodologies.
Responsibilities:
• Design and build complex, state-of-the-art agentic workflows.
• Develop 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:
Required:
• 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.
• 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).
• Expert-level 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, NN) 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.
Preferred:
• 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.
Company:
Tempus is making precision medicine a reality by applying AI in healthcare, deriving insights from our expansive library of clinical data and molecular data. Founded in 2015, the company is headquartered in Chicago, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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