Senior Machine Learning Scientist

What if… you could join an organization that creates, resources, and builds life sciences companies that invent breakthrough technologies in order to transform health care and sustainability?

FL94 Inc., is a privately held, early-stage biotechnology company pioneering the emerging field of Protein Editing. At FL94 we create small molecules that edit protein structure and function to unlock presently undruggable targets and a broad array of therapeutic modalities. Our platform integrates novel small molecule chemistry and chemoproteomic discovery technologies with Machine Learning (ML) to enable generative design. FL94 is backed by Flagship Pioneering, bringing their courage, vision, and resources to guide FL94 from platform validation to patient impact. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us! 

Position Summary: 

FL94 is searching for a senior machine learning research scientist to develop predictive and generative AI to radically accelerate small molecule lead optimization.

Reporting directly to the CTO, you will collaborate with other ML scientists, engineers and medicinal chemists to design, develop and benchmark machine learning T models from public and in-house assay data. You’ll design and implement active learning MPO strategies to reduce the number of compound optimization cycles in DMTA.

This role will require deep experience in state-of-the-art machine learning model development, pharmacology, and software engineering.

What You'll Do

• Develop predictive ADMET/QSAR models: Design, build and/or fine-tune cutting-edge global and local models for potency, selectivity, and key ADMET properties using state-of-the-art architectures.
• Leverage publicly available foundation models (e.g., TxGemma) and data to augment sparse functional data. Fine tune internal state-of-the-art models and design objective functions for Multi-Property Optimization (MPO).
• Enable synthesis-aware design: Integrate retrosynthesis prediction and reaction modeling into the design process to ensure that generated molecules are readily synthesizable.
• Build robust ML infrastructure: Establish and maintain data pipelines, stringent benchmarks and validation frameworks for rigorous, prospective model evaluation. You'll be responsible for deploying models that directly impact project decisions in our drug discovery programs.

Requirements:

• Ph.D. in CS, computational chemistry, applied mathematics, statistics, physics, or related discipline
• 5+ years of ML drug discovery experience: Proven track record of applying machine learning to solve problems in lead optimization, such as QSAR/ADMET modeling, hit-to-lead, or active learning within a design-make-test-analyze (DMTA) cycle.
• Core ML & Python expertise: Strong proficiency in Python and modern deep learning frameworks like PyTorch/TF, with experience building and deploying production-level ML systems in a fast-paced drug discovery startup environment.
• Expertise in generative AI & Geometric Deep Learning: Demonstrated experience in developing and applying generative models (e.g., conditional flow matching, diffusion, VAEs) and graph-based or 3D (multi-task) neural networks for molecular applications.
• Proficiency with Multi-Modal Models: Experience applying multi-modal architectures to fuse molecular structures and assay data with biological context from text and other modalities, enhancing the predictive power for QSAR/ADMET properties.
• Deep domain knowledge: A solid understanding of medicinal chemistry principles (e.g., SAR, MPO) and cheminformatics toolkits (e.g., RDKit). You should be comfortable translating drug discovery challenges into precise machine learning problems.
• Driven and a bias-to-action mindset: A proactive and detail-oriented approach, with excellent cross-functional communication skills for working closely with an interdisciplinary team of chemists, biologists, and engineers.

Bonus Points If You Have...

• A strong publication record: A track record of first-author publications in premier machine learning or computational chemistry venues (e.g., NeurIPS, ICML, J. Med. Chem., JCIM) or relevant patents.
• Open-source contributions: Meaningful contributions to major open-source projects in cheminformatics or machine learning (e.g., RDKit, DeepChem, PyG, Hugging Face).
• Quantum-informed modeling: Experience leveraging quantum chemistry (e.g., DFT) to generate physics-based descriptors for building accurate and robust QSAR/ADMET models.
• MLOps & scalability: Practical experience with MLOps tools (e.g., MLflow, W&B, Flyte) and training models at scale on cloud infrastruc