Research Scientist / Applied Science | Frontier AI x Bio

Research Scientist / Applied Scientist | Frontier AI x Biology

We're partnering with a frontier AI research lab developing next-generation machine learning approaches for complex problems across biology and the life sciences.

Sitting at the intersection of machine learning, computational biology, generative modeling, and scientific discovery, the team is building advanced models designed to learn representations of complex biological systems and translate breakthroughs in AI into meaningful scientific applications.

They're looking for a Research Scientist / Applied Scientist to join a highly technical research environment working across fundamental model development and real-world biological applications.

The Research

The research spans generative and foundation models, multimodal representation learning, sequence and structure modeling, and protein and molecular systems. The work involves developing and scaling new learning approaches across complex biological data, with a strong emphasis on self-supervised learning, scientific evaluation, generalization, and using AI to uncover meaningful biological insight.

This is not a traditional applied AI role focused on deploying existing models. The emphasis is on developing, adapting, and rigorously evaluating state-of-the-art ML methods against difficult biological problems, helping bridge the gap between frontier AI research and scientific discovery.

What You'll Do

• Develop and apply modern deep learning approaches to complex biological problems
• Build and evaluate generative and foundation models across biological data modalities
• Design architectures and representations capable of learning meaningful biological structure
• Develop rigorous experiments and benchmarks to understand where models succeed, fail, and generalize
• Work with large-scale biological datasets and modern ML training infrastructure
• Translate emerging research ideas into models that can be tested against real scientific questions
• Collaborate across machine learning, computational biology, and the broader life sciences

What We're Looking For

• PhD or equivalent research depth in Machine Learning, Computer Science, Computational Biology, Bioinformatics, Biophysics, or a closely related discipline
• Strong foundations in deep learning, generative modeling, representation learning, or foundation models
• Experience developing, adapting, or evaluating modern ML models
• Strong Python skills and experience with frameworks such as PyTorch or JAX
• Experience working with biological, molecular, sequence, structural, or other high-dimensional scientific data
• Ability to move comfortably between research exploration and hands-on model development
• Evidence of strong research through publications, open-source contributions, or equivalent technical work

Particularly Relevant Backgrounds

AI for Biology * Generative Models * Foundation Models * Protein Modeling * Sequence Modeling * Representation Learning * Multimodal Learning * Computational Biology * Structural Biology * Self-Supervised Learning * Large-Scale Model Training

Why This Opportunity?

This is an opportunity to join a frontier research environment where advances in machine learning are directly connected to fundamental questions in biology.

Rather than simply applying established architectures to another dataset, you'll have the opportunity to investigate new modeling approaches, push the capabilities of modern generative and foundation models, and explore how advances in AI can translate into meaningful scientific discovery.

The environment should particularly appeal to researchers looking for high scientific ownership, ambitious ML problems, close interaction between AI and biology, and the opportunity to help shape the research direction of a growing team.