• Independent thinker with a strong sense of ownership and capability of driving research projects or engineering robust systems from first-principles-based conceptualization to state-of-the-art realization
• Experienced in contributing to challenging deep learning research projects, designing and iterating on novel model architectures, training and inference algorithms, etc
• Experienced with running, scaling up, and analyzing distributed training experiments
• Curious, problem-oriented thinker who is excited to dive deep into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries (no previous experience in anything but ML necessary)
• Strong engineer who constantly strives for technical excellence. You can write clean code and have a deep understanding of the codebases you work in

Desirable

• We’re seeking experienced ML research engineers to join the team and lead research and engineering efforts focused on driving forward our ML research agenda for generative modeling of molecular systems, which is instrumental to our mission
• As a ML Research Engineer at Genesis, you will drive forward novel ML experimentation, ideation, execution, and analyses of results on the path towards building larger and more powerful generative and predictive models of biomolecular systems of increasing complexity
• Blending together techniques from cutting edge machine learning (denoising diffusion, equivariant architectures, self-supervised training of multi-modal models, RL-based alignment) and computational physics/chemistry (molecular dynamics, quantum mechanics), you will lead rapid iteration on our AI platform unlocking the next level of performance, efficiency, and scale that was not previously possible
• Lead experimentation on transformative research projects that enhance the Genesis AI platform's capabilities, focusing on generative and predictive models for molecular systems
• Carefully design and run experiments at scale to validate most promising approaches, leading ML engineering efforts and shipping state-of-the-art models to production
• Navigate the latest deep learning literature, extracting insights from generative modeling in adjacent domains (language, images, 3D graphics, etc.), developing novel models and training techniques for molecular data, and communicating your findings to the team
• Have the opportunity to mentor and guide more junior members of our technical team as well as research interns, fostering an environment of growth and innovation