Principal Computational Biologist

Key Skills

Aartificial intelligence, Agile methodologies, AWS, AWS Sagemaker, Data management, Keras software library, Machine learning techniques, MxNet, Python Programming, PyTorch, R Programming, Statistics, TensorFlow

Job Description

FL89, Inc. is a privately held early-stage company that is leveraging advanced biological and computational tools to develop breakthroughs in our understanding of secreted protein function and regulation in human physiology. More specifically, Fl89 is pairing high throughput -omics technology with AI/ML based protein structure prediction to define and discover novel therapeutic protein biology.

Position Summary:

We are seeking an energetic and motivated scientist with extensive experience in computational structural biology and developing AI/ML models for generative protein design to join our tight-knit team. The ideal candidate has superior skills in these areas, particularly as it pertains to the interplay between protein sequence, structure, and high-dimensional NGS assays, and thrives in a fast-paced and collaborative startup environment. The position will provide a unique opportunity to play a critical role in the advancement of FL89’s computational discovery and preclinical platforms, through the development of disruptive approaches for biotherapeutic drug discovery.

Key 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.

Minimum Qualifications:

• 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

Preferred Qualifications:

• Experience with protein co-evolution analysis
• Experience with NGS and bioinformatics tools
• Experience with agile methodologies.

Flagship Pioneering is 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.