Principal Scientist
Computational and Structural Biology
Confirmed live in the last 24 hours
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

6%

1 year growth

15%

2 year growth

56%
Locations
Cambridge, MA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Agile
Python
Data Science
Tensorflow
R
Data Structures & Algorithms
Keras
Pytorch
AWS
CategoriesNew
AI & Machine Learning
Biology & Biotech
Requirements
  • PhD in computational biology (or equivalent)
  • 3+ years of experience implementing AI/ML models for generative protein design
  • 12+ years of industry experience in computational structural biology or a related field such as computational evolutionary biology, or protein engineering
  • Demonstrated industry experience or academic achievement, as evidenced by publications in high impact journals, conference proceedings, or widely used codebase
  • Proficiency with bioinformatics tools in the protein space (e.g. AlphaFold, ESM,USalign,Rosetta, etc.)
  • Strong familiarity with topics of protein and macromolecular structures and interactions with other proteins
  • Experience with cloud environments such as AWS
  • Experience in developing machine learning platforms using modern ML frameworks for deep learning (e.g. PyTorch, tensorflow, keras, MXNet) & deploying in services such as Amazon Sagemaker.
  • Experience with Statistical analysis tools in Python and R as well as data science app development (R shiny/Streamlit).
  • Knowledge of best practices in software engineering for reproducibility and data management skills
  • Ability to work in and adapt to a fast-paced environment
Responsibilities
  • Apply computational models and AI/ML approaches: Utilize computational models and develop AI/ML algorithms to analyze protein structure-function relationships and their impact on human health. This includes designing and implementing novel approaches to model protein structure, developing structural alignment tools, and assigning functional similarities.
  • Collaborate with experimental teams: Work closely with experimental teams combining computational insights with experimental data. Collaborate on the identification, classification, and validation of novel secreted proteins and protein functions.
  • Stay updated on protein space and industry trends: Keep abreast of the latest developments in computational structural biology, protein engineering, and related fields.
  • Keep up to date with bioinformatics tools in the protein space.
  • Develop and deploy machine learning platforms: Build and deploy machine learning platforms using modern frameworks for machine learning.
  • Cloud environment utilization: Utilize cloud environments, particularly in AWS, for computational tasks and data management.
  • Contribute to scientific publications and codebase: Contribute to scientific literature through publications in high impact journals and conference proceedings. Contribute to widely used codebases in the field.
  • Ensure best practices in software engineering for reproducibility and data management.
  • Maintain data management and reproducibility: Follow best practices in software engineering for data management and reproducibility, ensuring that the work is well-documented and easily reproducible.
  • Adaptability and fast-paced environment: Thrive in a fast-paced and collaborative startup environment, demonstrating flexibility and adaptability to evolving project needs and timelines.
Desired Qualifications
  • Experience with protein co-evolution analysis
  • Experience with NGS and bioinformatics tools
  • Experience with agile methodologies.