MLOps Engineer

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

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• Applied industry experience in MLOps, DevOps, or a related field
• Excellent programming skills in Python, with experience in ML frameworks
• Experience with containerization
• Experience with data pipelines, data warehousing, and ETL processes
• Experience with data versioning and data lineage tracking
• Strong understanding of ML model deployment, scaling, and management
• Excellent problem-solving skills, with the ability to debug complex issues
• Strong communication and collaboration skills, with the ability to work with cross-functional teams
• Experience with agile development methodologies and version control systems such as Git

Preferred Qualifications:

• Experience with MLOps platforms such as MLflow, TensorFlow Extended (TFX), or Kubeflow
• Experience with DevOps tools such as Jenkins, GitLab CI/CD, or CircleCI
• Experience with BigQuery

Benefits

• Competitive compensation package
• Private medical insurance (underwritten on a medical health disregarded basis)
• Life insurance (4 x salary)
• Individual training/development budget through Learnerbly
• Individual wellbeing budget through Juno
• 25 days holiday plus public holidays and 1 day birthday leave per year
• Hybrid working (home + office)
• Potential to have real impact and accelerated career growth as an early member of a multinational team that's building a transformative knowledge product

Be yourself at Causaly... Difference is valued. Everyone belongs.

Diversity. Equity. Inclusion. They are more than words at Causaly. It's how we work together. It's how we build teams. It's how we grow leaders. It's what we nurture and celebrate. It's what helps us innovate. It's what helps us connect with the customers and communities we serve.

We are on a mission to accelerate scientific breakthroughs for ALL humankind, and we are proud to be an equal opportunity employer. We welcome applications from all backgrounds and fairly consider qualified candidates without regard to race, ethnic or national origin, gender, gender identity or expression, sexual orientation, disability, neurodiversity, genetics, age, religion or belief, marital/civil partnership status, domestic / family status, veteran status or any other difference.