About us

Founded in 2018, Causaly accelerates how humans acquire knowledge and develop insights in Biomedicine. Our production-grade generative AI platform for research insights and knowledge automation enables thousands of scientists to discover evidence from millions of academic publications, clinical trials, regulatory documents, patents and other data sources… in minutes.

We work with some of the world's largest biopharma companies and institutions on use cases spanning Drug Discovery, Safety and Competitive Intelligence. You can read more about how we accelerate knowledge acquisition and improve decision making in our blog posts here: Blog - Causaly

We are backed by top VCs including ICONIQ, Index Ventures, Pentech and Marathon.

About the role:

The ML Ops Engineer will be responsible for designing, developing, and maintaining the infrastructure and tools that support our machine learning models. You will work closely with our data scientists, engineers, and product teams to ensure the smooth operation of our ML workflows, from data ingestion to model deployment.

Responsibilities:

• Design, implement, and maintain our ML infrastructure, including data pipelines, model training, and deployment workflows
• Develop and maintain tools for automating ML workflows, such as data pre-processing, feature engineering, and model evaluation

• Collaborate with stakeholders to optimize model performance, scalability, and reliability in production, including monitoring, logging, and troubleshooting
• Develop and maintain data quality checks and data validation pipelines
• Implement and maintain data versioning and data lineage tracking
• Stay up-to-date with the latest developments in ML Ops and recommend best practices and new technologies to the team