How to apply for Senior AI Researcher

About Recursion

Recursion is an AI-native drug discovery company. It has built one of the largest AI compute clusters in biopharma and a broad infrastructure across drug discovery and clinical development. The company aims to build a unifying biomedical world model that bridges virtual patients and virtual cells.

About the role

As a senior AI researcher, you will work on frontier AI research and end-to-end applications across discovery, design, and translation. You will join a cross-functional team of scientists, engineers, and drug hunters. The goal is to turn real-world data into a discovery engine that closes the loop between clinical care and drug discovery.

A typical day

A typical day might involve collaborating with biologists and engineers to formulate a research problem, then developing and testing AI models on large biological datasets. You could spend time analyzing results, writing code, and discussing findings with your cross-functional team. The work is remote, so much of the communication happens via video calls and written messages.

Who Recursion is looking for

• Has a strong publication record or equivalent experience in AI/ML research, especially in areas like representation learning, generative models, or multimodal learning.
• Has experience applying AI to biological or chemical data, such as genomics, transcriptomics, imaging, or molecular design.
• Can work across the full research pipeline, from problem formulation to implementation and validation.
• Is comfortable collaborating with scientists and engineers from different backgrounds in a fast-paced environment.

Tips for this application

• Highlight any experience you have with large-scale biological datasets and compute infrastructure, since Recursion operates one of the largest AI clusters in biopharma.
• Emphasize projects where you bridged virtual and real-world data, such as integrating patient data with cell models or clinical outcomes.
• Show familiarity with Recursion's public research, such as their papers on phenomics, self-supervised learning, or their map of biological relationships.
• In your resume, quantify the impact of your AI models in drug discovery or healthcare, such as improved hit rates or reduced time to lead.
• Address the remote nature of the role by demonstrating your ability to collaborate effectively across time zones and communicate clearly in writing.

What to cover in your cover letter

Explain why you want to work on building a biomedical world model that connects virtual patients and virtual cells. Describe a specific project where you applied AI to a biological problem and what you learned. Mention how you handle the combinatorial explosion of biological hypotheses with scalable AI methods. State what you hope to contribute to Recursion's discovery engine.

Research before applying

• Read Recursion's recent publications and blog posts to understand their technology stack and research directions.
• Look into their clinical pipeline and partnerships to see how AI is applied in practice.
• Investigate their compute infrastructure and data generation methods, such as their phenomics platform.
• Find interviews or talks by Recursion researchers to understand their team culture and technical challenges.

Likely interview topics

Based on the job description, expect questions about:

• How would you design an AI system to integrate virtual patient data with virtual cell models?
• Describe a time you scaled an AI model to handle massive biological datasets. What challenges did you face?
• What are the key limitations of current AI methods in drug discovery, and how would you address them?
• How do you evaluate the success of an AI model in a drug discovery context beyond standard ML metrics?
• Discuss a recent paper from Recursion or a similar company. What did you think of their approach?

Common mistakes to avoid

• Focusing only on generic AI achievements without linking them to biological or drug discovery applications.
• Ignoring the scale of data and compute involved; Recursion works with petabytes of biological data and massive clusters.
• Applying without any knowledge of Recursion's specific approach to AI-native drug discovery, such as their focus on phenomics and world models.