Overview Join FL129 as a Senior Scientist to help build a novel platform for generative chemistry and molecular modeling. You will work with a cross-disciplinary team of experimental and computational scientists to advance data-driven drug discovery. Your work shapes the scientific direction and platform capabilities, collaborating closely with wet labs and translating results into actionable insights. This is an opportunity to lay the foundation of a transformative biotech platform in an entrepreneurial environment.
Compensation / Benefits- healthcare coverage
- annual incentive program
- retirement benefits
- broad range of other benefits
- weekly lunches
- monthly social events
Responsibilities- Technology development aligned with FL129's core strategic goals and influence on the scientific direction as an early hire
- Develop, train, validate, and apply novel generative chemistry and molecular models
- Curate training and benchmark datasets; design and implement rigorous benchmarks to assess model performance
- Optimize existing workflows and establish best experimental and documentation practices for platform excellence
- Collaborate with wet lab science team to drive key project goals
- Translate experimental data into meaningful insights and present results at team meetings
Key requirements- Ph.D. in machine learning, computational biology, or a similar field with 5+ years of industry or postdoctoral experience
- Familiarity with SOTA models such as Boltz-2, ESM-2, Chai-2 and related
- Experience training and fine-tuning molecular models for tasks such as protein language modeling, protein structure prediction, binder prediction, protein design/engineering, small molecule pose prediction
- Experience curating publicly available datasets, including mutational scanning data
- Proficiency in Python and modern HPC and cloud compute workflows
- Nice to have: Deep structural understanding of proteins
- Motivated team-player with strong organizational and written/verbal communication skills
- collaboration
- strong communication
- team-player
- machine learning for molecular modeling
- generative chemistry
- molecular modeling
