About the job
About Us:
Calico Life Sciences LLC, a pioneering research and development company founded by Alphabet, is dedicated to leveraging advanced technologies and modeling systems to deepen our understanding of the biology governing human aging. Our mission is to utilize this knowledge to create interventions that empower individuals to enjoy longer, healthier lives. With state-of-the-art technology labs, a firm commitment to curiosity-driven discovery science, and a dynamic drug-development pipeline in collaboration with academic and industry partners, Calico is an exhilarating environment for fostering medical breakthroughs.
Role Overview:
We are looking for a Senior / Staff Machine Learning Infrastructure Engineer to transform machine learning algorithms into practical applications and to construct a state-of-the-art computing and machine learning platform for analyzing biological datasets and modeling macromolecular sequences and structures. You will play a crucial role in a cross-functional initiative aimed at developing a premier computing and data analysis platform that underpins Calico's research endeavors through the following responsibilities:
- Develop and Optimize: Design and implement software infrastructure and tools that accelerate ML research, manage training and inference workflows, and streamline data ingestion and curation processes.
- Define Engineering Best Practices: Spearhead efforts to reorganize and refactor existing research codebases, enhancing code quality and reproducibility.
- Collaborate for Impact: Partner closely with our ML research scientists to deploy models that tackle critical questions in aging research and further Calico's mission.
The ideal candidate will have extensive experience in designing, developing, and maintaining scalable and reliable ML infrastructure components, including data pipelines, model training and deployment systems, and monitoring tools. You should possess a proven track record of optimizing ML workflows for both performance and resource utilization, and remain updated on best practices in ML model management, versioning, and reproducibility. Effective communication and collaboration across functions are essential for executing complex projects successfully.
