Senior – Machine Learning Scientist
Materials Characterization
Posted on 3/20/2024
Flagship Pioneering

501-1,000 employees

Originates biotech ventures for health and sustainability
Company Overview
Flagship Pioneering stands out as a leader in the biotechnology industry, having originated and nurtured over 100 scientific ventures, including high-profile companies like Moderna and Indigo Agriculture. The company's culture is rooted in developing transformative products for human health and sustainability, as evidenced by their creation of platform companies like Generate Biomedicines and Tessera Therapeutics. Their competitive edge lies in their ability to explore new frontiers in genetics, as demonstrated by their recent unveiling of Quotient Therapeutics, a venture aimed at creating transformative medicines.
Venture Capital

Company Stage

N/A

Total Funding

$6.4B

Founded

2000

Headquarters

Cambridge, Massachusetts

Growth & Insights
Headcount

6 month growth

7%

1 year growth

22%

2 year growth

45%
Locations
Cambridge, MA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Python
Data Science
Tensorflow
Pytorch
Pandas
NumPy
CategoriesNew
AI & Machine Learning
Applied Machine Learning
AI Research
Requirements
  • Experience developing and applying ML models to learn on experimental scientific data
  • Experience in chemical and materials characterization techniques (spectroscopy, crystallography, microscopy)
  • Strong experience in at least one ML framework (PyTorch/TensorFlow/Jax) and robust experience in Python data science ecosystem (Numpy, SciPy, Pandas, etc.)
  • Experience using a cloud computing service to reduce runtime to train and evaluate deep learning models
  • PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field
  • Strong self-starter and independent thinker, with strong attention to detail
  • Demonstrated industry experience or academic achievement
  • Excellent communication and presentation skills, capable of conveying technical information in a clear and thorough manner
  • Eager to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting
Responsibilities
  • Design, build and scale machine learning models to automatically processes large-scale materials characterization data
  • Leverage digitized materials characterization and performance data to close data-driven design-make-test feedback loops
  • Use ML to detect and quantify sources of noise and bias in experimental data and uncertainty in ML models
  • Combine AI/ML methods with first principles and modeling efforts to accelerate inversion of experimental characterization data
  • Work with the computational team to identify materials design pathways that target desired functional properties
  • Work with infrastructure and automation teams to transfer data and predictions in real time
  • Work with the experimental team to drive material discovery and development
  • Continually cultivate scientific/technical expertise through critical review of ML literature, attending conferences, and developing relationships with key opinion leaders
  • Report findings to the FL96 research team