Summary
Genesis Therapeutics is seeking an experienced ML Research Scientist to lead generative modeling research for molecular systems. The role involves shaping the research agenda, driving continuous evolution of the AI platform, collaborating with experts, publishing findings, mentoring team members, and having opportunities for growth. The ideal candidate should be a deep learning expert with a track record in relevant domains.
Requirements
- A deep learning expert with a track record of doing novel research in one or more of the following domains: denoising diffusion, normalizing flows, flow matching, neural ODEs and SDEs, or other cutting edge generative or predictive machine learning models
- Independent thinker with a strong sense of ownership and capability of driving research ideas from first-principles-based conceptualization to state-of-the-art realization
- Strong coder who is not afraid of deep dives into engineering whenever necessary
Responsibilities
- Lead transformative research projects that enhance the Genesis AI platform's capabilities, focusing on generative models for molecular systems
- Navigate the latest deep learning literature, extract insights from generative modeling in adjacent domains, develop novel models and training techniques for molecular data, and communicate findings to the team
- Carefully design and run experiments at scale to validate most promising approaches and work closely with engineers to ship state-of-the-art models to production
- Contribute to the research community by publishing some of our findings in top tier AI/ML venues, attending conferences and workshops, and engaging in continuous learning and knowledge exchange
- Mentor and guide more junior members of our technical team as well as research interns, fostering an environment of growth and innovation
Preferred Qualifications
- PhD in machine learning, computer science, other computational sciences or equivalent research experience and strong publication record at top tier ML venues are preferred
- Hands-on experience with some of the core libraries we use: Pytorch, Pytorch Lightning, Pytorch Geometric
- Experience in distributed training and inference of large models on GPU clusters
- Experience working with some of the following: molecular systems (protein sequences and 3D structures, small molecules, etc.), ML force fields or other physics-informed models and methods, or point cloud data in other application domains, such as 3D graphics
Benefits
- Competitive compensation package that includes salary and equity
- Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees)
- 401(k) plan
- Open (unlimited) PTO policy
- Free lunches and dinners at our offices
- Paid family leave (maternity and paternity)
- Life and long- and short-term disability insurance
