About VantAI:
VantAI pairs bleeding-edge machine learning techniques with deep systems biology expertise to build computational models that uncover hidden relationships between molecules, targets, and diseases. These models power a best-in-class solution that identifies and generates new molecular entities for targets of interest, repurposes existing molecules at any stage of development, uncovers accurate ADME and toxicological insights, and predicts adverse events likely to influence trial success from deep analysis of systems-based pharmacogenomics. VantAI's in silico platform specializes in modeling complex protein-protein interactions, powering the discovery of biologics and protein degraders in addition to small-molecule drugs. It has helped leading biopharma partners launch new development programs--or revitalize old ones--at a fraction of the time and cost of traditional methods.
About You:
We are looking for talented engineers with an insatiable hunger for solving bleeding-edge scientific problems spanning biology, chemistry, physics, data science and computational science domains to join our core application team.
Successful candidates will develop large-scale scientific workflows, provide support to our scientists on existing workflows and proactively identify areas for increasing automation to reduce inefficiencies. Projects may include developing cheminformatics tools and algorithms; large-scale virtual screening and protein-protein docking; creating scalable on-demand ML inference infrastructure; developing scalable chemical databases; automation of modeling, analysis, and visualization of simulations; and a range of similar tasks integral to achieving our mission of becoming the leading AI-enabled drug discovery in the field of induced proximity.
Our tech stack spans several languages and frameworks, including but not limited to Python, Go, Rust, React, running in Docker/Kubernetes on GCP. Relevant areas of expertise might include workflow engineering, tool integration, high performance cloud computing, systems engineering, and machine learning, but specific knowledge of any of these areas is less critical than versatility and a willingness to learn and work anywhere in the tech stack. We value individuals who want to make an impact, have a deep intellectual curiosity, enjoy solving challenging problems and have a track record of achievement.
Specific Skills and Qualifications:
- Experience working in complex codebases and cloud-native architectures
- IAM policy development and automation
- Container orchestration and Kubernetes cluster development
- Experience supporting large-scale scientific workflows/compute environments
- Data architecture and engineering
- Ability to break apart existing code; solve ad hoc problems with little help
- Willingness to work with existing codebases
- No task too big or small; strong sense of independence and ownership
- Provide guidance and opinions on implementation/considerations, while having a willingness to compromise
- Developing cheminformatics workflows/algorithms (desired)
- Experience working on drug discovery projects (desired)
Salary: $120,000 - $190,000
This band is a reflection of the job description as written. Looking for a higher salary? Apply anyway! We are happy to speak to more experienced candidates who may require a higher salary and discuss that experience in our first touchpoint.
Top Skills
What We Do
VantAI pairs bleeding-edge machine learning techniques with deep systems biology expertise to build computational models that uncover hidden relationships between molecules, targets, and diseases. These models power a best-in-class solution that identifies and generates new molecular entities for targets of interest, repurposes existing molecules at any stage of development, uncovers accurate ADME and toxicological insights, and predicts adverse events likely to influence trial success from deep analysis of systems-based pharmacogenomics. VantAI's in silico platform specializes in modelling complex protein-protein interactions, powering the discovery of biologics and protein degraders in addition to small-molecule drugs. It has helped leading biopharma partners launch new development programs--or revitalize old ones--at a fraction of the time and cost of traditional methods. For more information, please visit http://www.vant.ai.
