Senior/Principal AI Scientist, Foundational ML, AI for Biology & Translation (AIBT)

In this role, you will:

• Design and build foundation models to support target and drug discovery, with a focus on large-scale representation learning, multimodal mid-training and post-training, reasoning, and reinforcement learning.
• Develop scalable approaches to curate, organize, and integrate diverse data modalities such as molecular structures, biological sequences, omics data, biochemical readouts, and text into high-quality training data and evaluation tasks.
• Develop models and agentic systems that propose experiments, interpret results, and learn from experimental feedback, advancing lab-in-the-loop discovery.
• Build training and evaluation environments from biological data to develop and measure scientific reasoning and discovery capabilities.
• Scale state-of-the-art models to massive datasets, working at the intersection of deep learning and engineering, from distributed training and efficient experimentation to reliable data pipelines, in collaboration with engineering and MLOps teams.
• Publish in top-tier ML venues and scientific journals, and present results at internal and external conferences and workshops.
• For the Principal AI Scientist level: Define and drive larger research initiatives within these areas, turning open scientific challenges into coherent research agendas and delivering advances from initial ideas through rigorous evaluation and scientific impact.
• For the Principal AI Scientist level: Set technical direction across related projects and resolve critical modeling, data, and engineering challenges while remaining deeply involved in hands-on research and development.

Who you are:

• Educational background: Ph.D. in Computer Science, Machine Learning, Computational Biology, or a related quantitative field.
• Experience: Proven track record advancing AI models in research and/or industry settings, with deep expertise in one or more of large-scale representation learning, multimodal generative models, LLMs, AI agents, or reinforcement learning.
• Strong motivation to push the frontier of AI for science and translate advances in AI into meaningful progress in biology and drug discovery.
• Prior experience applying AI to biology, chemistry, or drug discovery is a plus, especially with multimodal experimental data or experimental design.
• Technical skills: Excellent knowledge of the theory and practice of deep learning, with strong research intuition and the ability to turn open scientific questions into rigorous experiments.
• Proven experience developing and delivering innovative ML solutions in the areas above.
• Excellent Python programming skills, fluency with modern agentic coding environments, and extensive experience with AI frameworks such as PyTorch or JAX.
• Strong grasp of software engineering, data engineering, and MLOps best practices, including scalable data processing, high-performance computing, and reproducible training and evaluation workflows.
• Strong publication record and active contribution to research communities, including top-tier ML venues such as NeurIPS, ICML, and ICLR, and/or a public portfolio demonstrating substantial contributions to relevant AI projects.
• Excellent communication, collaboration, and problem-solving skills.
• Preferred: Practical experience bridging innovative ML methods and applications in target/drug discovery.
• Preferred: Experience with biological and chemical modalities and tasks such as molecular structures, single-cell/omics data, perturbation biology, and multimodal biological datasets.
• Preferred: Hands-on experience developing, finetuning, and optimizing LLMs and agentic systems.
• For the Principal AI Scientist level: Typically 3+ years of post-Ph.D. research experience in relevant AI areas, or an equivalent portfolio of research contributions demonstrating comparable depth, independence, and impact.
• For the Principal AI Scientist level: A strong track record of driving complex research initiatives with substantial scientific or technical impact, demonstrating the judgment to identify high-value problems and make consequential technical decisions.