Applied Machine Learning Scientist, Biology

At Flagship Pioneering, we conceive, create, resource, and develop first-in-category life sciences companies to transform human health and sustainability. We’ve created over 100 scientific ventures, including the now familiar drug and vaccine innovator, Moderna Therapeutics.

Since its inception in 2000, many of our companies have leveraging advances in computing, big data and AI. In recent years, this trend has accelerated with first-in-category life science companies such as Generate Biomedicines, Cellarity, Valo and many others that are creating breakthrough innovations using AI and ML technologies.

We are looking for extraordinary computational scientists, engineers, and entrepreneurs to work alongside individuals within the Flagship Ecosystem focused on solving the most impactful challenges in AI and the life sciences.

Position Summary:

Our Applied ML Scientists will apply state-of-the-art deep learning (DL) tools and methods to tackle problems from multiple biological (life sciences) disciplines/domains.  They will work closely with an interdisciplinary team of ML scientists, biologists, and engineers to design and implement novel ML tools with biological impact and evaluate their strengths and limitations.  This is an exciting opportunity to be part of a fast-paced, highly dynamic entrepreneurial environment.

Key Responsibilities:

• Work with interdisciplinary team of ML scientists, biologists and engineers to develop and apply novel ML and DL models on various biological problems and datasets
• Study internal and external datasets to address questions critical to Flagship’s core objectives and generate testable hypotheses.
• Design, plan, and execute experiments that support model validation and establish strengths and limitations
• Develop clear, intuitive visualizations. Communicate analysis results via presentations to a multi-disciplinary audience
• Cultivate a data-centric and process-oriented company philosophy by creating and maintaining best practices for software development, data management, and infrastructure
• Monitor and evaluate new and emerging technologies and models and identify opportunities for collaboration within Flagship Pioneering companies, academia, and third-parties.

Basic Requirements:

• PhD or equivalent level of experience in quantitative biology with at least one significant project leveraging machine learning (ML) methods or models.  Examples include projects in system biology, transcriptomics, genomics, biophysics or neuroscience. PhD may be in (1) a field directly relevant to ML (e.g. Machine Learning, Statistics, Computer Science, Mathematics) or (2) natural sciences (e.g. Physics, Computational Biology/Chemistry, Biology).
• Knowledge and experience applying deep learning (DL) models to biological data
• Fluency in Python and standard ML tools and packages (e.g. Deep Graph Library, PyTorch, Snorkel, etc.)
• Familiarity with AWS, GCP, or similar cloud-computing services
• Motivated and team oriented, with an ability to thrive in an entrepreneurial and multidisciplinary environment.
• Ability to independently lead and run research projects, while maintaining close communication with team members
• Excellent communication and presentation skills. Must be able to speak and ideate with multi-disciplinary team including biologists.  Must be able to think independently, work collaboratively and contribute to an active intellectual environment.

Preferred Requirements:

• Experience in one or more of the following areas/topics: graph neural networks, NLP (e.g. LSTMs, Transformers), CNNs, variational methods or GANs
• Familiarity with containerization and task orchestration tools (e.g. Docker, Kubernetes, Slurm)
• Experience working with major biological databases and datasets (e.g. TCGA) is a strong plus