Scientist/Senior Scientist, Machine Learning (Biomolecule Design)

Job Application For Research Associate / Senior Research Associate, Vaccine Researchflagship Pioneering, Inc. - Cambridge, MA

Full-time - Senior

Cambridge, MA

About the position

FL97 is seeking an experienced, creative, and talented (Senior) Machine Learning Scientist, Biomolecule Design to join our team. In this role, you will be part of an agile, cross-functional team responsible for generating protein, RNA, or DNA sequences, updating models with experimental data, and integrating machine learning models into the data pipelines for our labs. You will collaborate closely with biologists, bioinformaticians, software developers, and automation engineers to model biological sequences across a variety of valuable therapeutic applications. The ideal candidate will have a strong machine learning background with experience in the biotech industry, particularly in building generative ML models, pre-processing pipelines, and comprehensive benchmarks to support biological sequence design. Your responsibilities will include designing, training, and fine-tuning deep learning models to engineer proteins, RNA, or DNA. You will implement rigorous testing, documentation, and model benchmarking, and incorporate these models into AI-enabled toolchains for the biology lab. Additionally, you will work collaboratively with cross-functional teams to translate ML model predictions into evaluated sequences by the wet lab. This position offers a unique opportunity to leverage advanced technology in AI experimental design and simulation, automated liquid handling and instrumentation, and generative molecular design, contributing to the future of science and therapeutic discovery.

Responsibilities

• Generate protein, RNA, or DNA sequences using machine learning techniques.
• Update models with experimental data to improve accuracy and performance.
• Integrate machine learning models into data pipelines for laboratory applications.
• Design, train, and fine-tune deep learning models for biomolecule engineering.
• Implement rigorous testing, documentation, and model benchmarking processes.
• Collaborate with biologists, bioinformaticians, software developers, and automation engineers to translate ML predictions into evaluated sequences.

Requirements

• PhD in applied mathematics, computer science, physics, computational biology, or other quantitative disciplines.
• Proven track record in developing deep learning models.
• Experience with large language models (e.g., autoregressive LLMs) for biological sequences.
• Expertise in at least one ML framework (PyTorch/TensorFlow/Jax).
• Robust experience in the Python data science ecosystem.
• Experience in training and deploying ML models on distributed computing services.

Nice-to-haves

• Experience in the biotech industry building generative ML models.
• Interest in collaborating with cross-functional teams in a laboratory setting.

Benefits

• Access to advanced technology in AI experimental design and simulation.
• Opportunities for collaboration in a cross-functional team environment.