FL85 is a biotechnology company seeking to transform the current approach to information molecule therapeutics by developing novel ML approaches to optimize in vivo therapeutic delivery vehicles to enable the next generation of genomic medicines.
Requirements
- Strong experience with uncertainty quantification, active learning, and Bayesian Optimization in drug delivery, materials science, or related fields
- Proficiency in ML frameworks (PyTorch/TensorFlow/JAX) and the Python data science ecosystem (NumPy, SciPy, Pandas, etc.)
- Hands-on experience with cloud computing (e.g., AWS, GCP, Azure) to accelerate model training and deployment
- Experience with AWS services for ML training, workflow orchestration, and data management
- Familiarity with integration of ML models into experimental workflows
- Background in drug delivery, lipid chemistry, or nanoparticle formulation
Responsibilities
- Design, build, and scale supervised ML models for active learning and Bayesian Optimization of lipid nanoparticle (LNP) formulation, synthesis, and in vivo performance
- Develop and implement best practices for uncertainty quantification in LNP potency and biodistribution
- Integrate multi-fidelity datasets (in silico and in vivo) to accelerate data-driven discovery of novel LNP chemistries
- Collaborate with computational scientists to identify design pathways for LNPs that achieve targeted functional properties (e.g., potency, tissue tropism, tolerability) and guide their synthesis
- Work with infrastructure and automation teams to streamline real-time data transfer between predictive models and experimental platforms
- Partner with experimental teams to drive iterative LNP design–make–test–analyze (DMTA) cycles, including development of domain-specific acquisition functions for multi-objective optimization
- Communicate findings to stakeholders and leadership through written reports and technical presentations
Other
- PhD in Computer Science, Applied Mathematics, Bioengineering, Chemical Engineering, or a related quantitative field with a strong ML focus
- 4+ years of ML-based in industry and/or academic settings
- Strong independent problem-solving ability and attention to detail
- Demonstrated achievement in industry or academia (publications, patents, or successful ML-driven projects)
- Excellent communication and presentation skills for both technical and interdisciplinary audiences
- Enthusiasm for working with cross-functional teams of experimentalists, engineers, and computational scientists in a fast-paced, entrepreneurial environment
