(Sr.) Machine Learning Scientist, Biological Sequence Design

The Company

Flagship Labs 97, Inc. (FL97) is privately held, early-stage biotechnology pioneering the use of artificial intelligence to transform all aspects of the scientific method. FL97 is backed by Flagship Pioneering, which brings the courage, long-term vision, and resources needed to realize unreasonable results.

FL97 is at the forefront of advancing AI-driven science, biological sequence design, and lab automation. Join our mission-driven team and contribute to the future of biotech.

The Role

FL97 is seeking an experienced, creative, and talented (Senior) Machine Learning Scientist, Biological Sequence Design to join our team. 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 ML models into the data pipelines for our labs. Together with biologists, bioinformaticians, software developers, and automation engineers, you will work toward modeling biological sequences across a variety of valuable therapeutic applications.

The ideal candidate has a machine learning background with biotech industry experience building generative ML models, pre-processing pipelines, and comprehensive benchmarks to support biological sequence design.

Candidates should have experience and interest in:

• Designing, training and fine-tuning deep learning models to engineer proteins, RNA or DNA.
• Gathering and pre-processing public datasets with bioinformatics tools to pre-train ML models.
• Implementing rigorous testing, documentation, and model benchmarking.
• Incorporating these models into AI-enabled toolchains for the biology lab.
• Collaborating with cross-functional teams to translate ML model predictions into evaluated sequences by the wet lab.

Qualifications:

• 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) and robust experience in the Python data science ecosystem.
• Experience in training and deploying ML models on distributed computing services (e.g. AWS/GCP/Azure, or clusters).

Working at FL97, you would have access to advanced technology in the areas of:

• AI experimental design and simulation
• Automated liquid handling and instrumentation
• Generative molecular design

More About Flagship Pioneering

Flagship Pioneering conceives, creates, resources, and develops first-in-category life science platform companies to transform human health and sustainability. Since its launch in 2000, the firm has, through its Flagship Labs unit, applied its unique hypothesis-driven innovation process to originate and foster more than 100 scientific ventures, resulting in over $100 billion in aggregate value. To date, Flagship has deployed over $3.1 billion in capital toward the founding and growth of its pioneering companies alongside more than $19 billion of follow-on investments from other institutions. The current Flagship ecosystem comprises  transformative companies, including  Moderna (NASDAQ: MRNA), Sana Biotechnology (NASDAQ: SANA), Seres Therapeutics (NASDAQ: MCRB), Axcella Health (NASDAQ: AXLA), Denali Therapeutics (NASDAQ: DNLI), Foghorn Therapeutics (NASDAQ: FHTX), Indigo Ag, Generate Biomedicines, Tessera Tx, and others.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.