Scientific Software Engineer

No sub-contracting allowed ! Direct W/2 only. Be local to SF Bay area as it requires you to be onsite 3 days a week !!

Scientific Software Engineer

Scientific Software Engineer

In the small molecule team within Prescient Design, we develop machine learning methods to accelerate small-molecule drug discovery across clients Research and Early Development. Methods we deploy propose new molecules, score designs to prioritize the most promising compounds, analyze the results of high-throughput experimental methods, automate labs to increase experimental throughput, accelerate physics-based calculations, and more.

We are seeking a highly motivated Scientific Software Engineer to join our team to build workflows and user interfaces for computational and medicinal chemists. The successful candidate will collaborate extensively with computational and experimental researchers across clients to deploy and deliver python-based solutions for small molecule drug discovery.

The Role

• Work as a scientific software engineer to implement python-based workflows for cheminformatics and computational chemistry tasks to support our Lab-in-the-Loop efforts for small molecule drug discovery.
• Use software best practices (version control, testing, modular code development, documentation, etc.) to collaborate on a large codebase with our team of methods developers.
• Deploy workflows on HPC and cloud platforms and deliver user-friendly web-based interfaces to medicinal chemists across gRED and Roche.
• Benchmark and deploy new machine learning techniques for small molecule potency prediction.

Desired Qualifications

• BS, MS, or PhD degree in a computational field (e.g. Computer Science, Engineering or related field) with 1+ years of industry experience.
• Expert in Python and experience with scientific software development.
• Experience with deploying software workflows on cloud and HPC platforms.
• Experience working on collaborative code bases, including merge requests, code review, writing tests etc.
• Highly-motivated and independent self starter that is eager to collaborate.
• Excellent communication and interpersonal skills.
• Basic understanding of modern machine learning methods including predictive models, generative models, and active learning as applied to small molecule drug discovery.

Additional Qualifications

Candidates may additionally have, but are not required to have:

• Public portfolio of projects available on GitHub.
• Experience with RDKit or OpenEye Toolkits.
• 3+ years of industry experience.
• Extensive experience working with large chemical and biological datasets, including graph, sequence, and structure-based data.
• Demonstrated experience with modern Python frameworks for deep learning like PyTorch.
• Experience deploying microservices on kubernetes.
• Experience with workflow engines such as Apache Airflow or Dagster.