Senior Scientist, Machine Learning

Key Skills

Amazon Elastic Compute Cloud-EC2, Amazon Simple Storage Service (S3), AWS, AWS Sagemaker, Data pipelines, Data science techniques, Deep Learning, Effective communication skills, GPUs, HuggingFace, JAX framework, Keras software library, Machine learning techniques, Pandas, Python Programming, PyTorch, TensorFlow

Job Description

Position Summary:

FL94 is seeking a talented (Senior) Machine Learning Scientist. The successful candidate will develop and apply cutting-edge machine learning (ML) paradigms across FL94’s proprietary chemoproteomics and small molecule chemistry data sets to narrow the search space and increase the probability of discovering novel chemistries via generative design. This position is ideal for someone with broad and deep expertise in machine learning, deep learning, and computational sciences.

Key Responsibilities:

• Translate small molecule and proteomic drug discovery challenges into ML problems
• Maximize value of public and proprietary data assets
• Develop and prototype novel ML algorithms for impactful drug discovery problems
• Train and validate large deep learning models at scale
• Leverage open-source deep learning models and transfer learning to reduce time to solution
• Speed up training of large deep learning models using clusters of CPUs and GPUs
• Develop optimized data pipelines to fuel deep learning models
• Collaborate closely with experimental biologists, proteomics, chemists and computational biologists to focus efforts on impactful problems
• Communicate findings to a multi-disciplinary audience

Qualifications:

• PhD in applied mathematics, computer science and other quantitative disciplines with a strong focus in machine learning and deep learning
• Proven track record of innovation in applied mathematics, data science and ML
• 5-10+ years hands-on experience in implementing, evaluating and tuning hyperparameters for large deep learning models 
• Strong expertise in multiple programming paradigms and software libraries such Python, JAX, Tensorflow (or Pytorch), Keras, Pandas, Sklearn, Hugging Face
• Experience using AWS services such as S3, EC2, ECS, AWS Batch, Sagemaker to reduce runtime to train and evaluate deep learning models
• Experience in biotech/pharma, or small molecule drug design is ideal but not required. Candidate must have experience in solving real world problems using deep learning in a multidisciplinary setting
• Foundational understanding of biology and chemistry is ideal, but not required
• Strong written and oral communication skills

Location: Cambridge, MA

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.

Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. 

At Flagship, we recognize there is no perfect candidate. If you have some of the experience listed above but not all, please apply anyway. Experience comes in many forms, skills are transferable, and passion goes a long way. We are dedicated to building diverse and inclusive teams and look forward to learning more about your unique background.