ML Research Scientist, Foundation Models

About the Team

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.
• Collaborate intensely with a multidisciplinary team to translate your research into tangible impact on our drug discovery platform.
• Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.
• Mentor and guide other researchers and engineers, fostering a culture of shared learning, growth, and innovation.

You are

• A deep learning expert with a portfolio of novel research in one or more cutting-edge domains of generative or predictive modeling (e.g., diffusion, flow matching, RL, foundation model architectures and training methods).
• An independent, first-principles thinker with a strong sense of ownership over your research projects and a drive to see them through to completion.
• A strong coder, comfortable with going deep into the engineering stack to build, debug, and ship your own models.
• A curious, problem-oriented mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. No prior experience in biology or chemistry is necessary – only willingness to learn.
• A true team player with strong communication skills who thrives in highly collaborative, mission-driven environments where science and engineering are deeply intertwined.
• Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.

Nice to have's

• PhD in machine learning, computer science, other computational sciences or equivalent research experience, demonstrated by a strong publication record.
• Hands-on experience with Pytorch, Pytorch Lightning, Ray Distributed Training, Pytorch Geometric, etc.
• Experience in distributed training and inference of large models on GPU clusters.
• Familiarity with molecular data, (proteins, small molecules), physics-informed ML, or 3D point cloud data.