Senior Software Engineer, Full Stack
Remote Work Possible? -
We’re looking for a Senior Software Engineer to help us build the environment that powers the next era of life-saving treatments for patients. Our software engineers work side-by-side with our machine learning engineers, computational chemists, and medicinal chemists to achieve drug discovery objectives, spanning our entire tech stack. We are looking for mission-driven individuals that are capable of rapidly bringing ideas from 0 to 1 and eager to apply software engineering skills to life-saving cures.
If you enjoy challenges like the ones below, we’d love to hear from you!
- Architecting a Django REST-driven internal application development ecosystem that supports multiple drug development programs.
- Building performant software for billion-scale molecular analyses alongside machine learning engineers.
- Owning and leading code deployments, testing, and maintenance.
- Designing and implementing front-end interfaces that enable our in-house chemistry team to interface with software.
- Connecting Docker-based microservices and serverless scripts to enable automated dataset ingestion pipelines that speed up the pace of model development and serving.
- Leading a team of software engineers in adopting industry best-practices for web technologies.
- Designing data APIs to power machine learning models, visualization tools, and chemistry software.
We don’t have a hard set of background requirements, but generally we most value skills and experience in the following areas:
- We are ideally looking for folks with 5+ years of industry experience.
- Python development: Strong experience building production systems in Python, especially in a microservices or serverless environment.
- Containerization: Experience in using Docker and Kubernetes to containerize and launch microservices. ML-specific experience not required or expected.
- Web development experience - Ability to rapidly prototype and launch internal-facing web applications using frameworks like Django.
- ML in Production: Knowledge of best-practices for building automated data ingestion and model deployment pipelines
- Most importantly, an eagerness to learn new skills, wear many hats, and collaborate closely with a growing team of people.