ML Research Scientist

ML Research Scientist, Foundation Models

About the Team

Join a world-class team at the forefront of AI and biochemistry.

At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that will unlock groundbreaking therapies for patients with severe diseases.

We don’t just apply machine learning to biology; we are conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field. The Genesis AI team is building an engine for this revolution. You will work side by side with the top multidisciplinary researchers to design and build generative and discriminative foundation models at scale from the entire spectrum of molecular data, having access to ample compute and large-scale simulations.

About the Role

This is an opportunity for a scientific innovator to advance the future of generative AI in drug discovery. As a key member of the Genesis AI team, you will shape and drive our research agenda for foundation models. You will lead critical research initiatives in areas like reinforcement learning, novel model architectures, and advanced pretraining and post-training methods. Your core mission is to create groundbreaking models and insights that are instrumental in discovering new medicines.

This role requires a deep curiosity and a collaborative spirit. You will be a strong team player, working in close partnership with our exceptional engineers and drug discovery experts to bring complex ideas to life. We also want you to be a voice in the scientific community. We will actively support and champion your efforts to publish some research breakthroughs in premier ML venues such as NeurIPS, ICML, and ICLR.

Positions are available at various levels of seniority: Senior, Staff, and Principal.

You will

• Lead transformative research projects from conception to implementation, tackling core challenges in generative modeling for molecular systems.

• Design and develop novel models and algorithms, building upon the latest literature in diffusion models, flow matching, RL, LLMs, and other cutting-edge areas.

• Execute ambitious experiments at scale, leveraging our world-class computational infrastructure to rigorously validate your hypotheses and push the boundaries of the state-of-the-art.