Sr. Principal Software Engineer

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profile Job Location:

Burlingame, CA - USA

profile Monthly Salary: Not Disclosed
Posted on: 4 hours ago
Vacancies: 1 Vacancy

Job Summary

Genesis Molecular AI Inc. has an opening for a Sr. Principal Software Engineer in Burlingame CA.

The position duties are as follows: Design build and maintain tech systems for developing and utilizing machine learning models used for the discovery of new medicines. Daily job duties include: Define the roadmap for improving infrastructure for training and deploying machine learning models. Act as the technology lead on machine learning research projects. Plan the development of the companys main web product that supports all of our internal chemistry activities. Architect the software infrastructure to support the companys machine learning and computational chemistry research teams. Write software to improve the companys platform including adding features to the website improving our backed compute infrastructure or executing and deploying machine learning/ computational chemistry research. Build tools for visualizing novel molecules and interpreting machine learning models molecular dynamics force fields and simulations. Provide engineering support for our active drug programs. Improve how chemists and biologists interact with our proprietary generative and predictive models. Increase the research velocity of the machine learning and molecular dynamics research teams.

The position requires a Bachelors degree in Computer Science Engineering Machine Learning Electrical Engineering a related field or foreign equivalent plus 4 years of experience as a software engineer in machine learning. Experience must include: 4 years of experience coding in Python and Javascript. 4 years of experience working with various databases including RDMS like postgres NoSQL and blob storage like S3 and GCS. 3 years of experience working with various infrastructure components including Kubernetes Spark Ray and Parquet used for distributing work across compute clusters. 3 years of experience working with machine learning packages including PyTorch Scikit-learn WandB Numpy and Pandas. 1 years of experience working with various biological and chemical datasets including experience with structure prediction property prediction and generative models. 1 years of experience with molecular dynamics packages including Schrodinger tools OpenMM Pymol Openeye and RDKit. 3 years of experience using Jupyter notebooks for experimentations and matplotlib for plotting.

Telecommuting permitted 2-3 times per week.

Offered Wage: $300000/year

Resumes to Ref. SW25

Genesis Molecular AI Inc. is an Equal Opportunity Employer.

About Genesis Molecular AI

Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. The companys generative and predictive AI platform GEMS (Genesis Exploration of Molecular Space) integrates AI and physics into industry-leading models to generate and optimize drug molecules including the breakthrough generative diffusion model Pearl for structure prediction. Genesis has raised over $300 million from leading AI tech and life science-focused investors signed multiple AI-focused research collaborations with major pharma partners and is deploying GEMS to advance an internal therapeutics pipeline for a variety of high-impact targets.

Genesis is headquartered in Burlingame CA with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.


Required Experience:

Staff IC

Genesis Molecular AI Inc. has an opening for a Sr. Principal Software Engineer in Burlingame CA.The position duties are as follows: Design build and maintain tech systems for developing and utilizing machine learning models used for the discovery of new medicines. Daily job duties include: Define th...
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Key Skills

  • Continuous Integration
  • Docker
  • Jenkins
  • Python
  • System Design
  • Agile
  • C/C++
  • Go
  • Systems Engineering
  • Software Development
  • Java
  • Distributed Systems