Software Engineer

About the Role

To strengthen our AI for Science (AI4S) team, we are looking for Software Engineers with a track record in developing production-grade, data-driven software solutions. You will design, build and operate the scalable cloud infrastructure and services - including the serving of our models - that our AI systems and agentic applications run on, and you will be accountable for keeping them reliable in production. This is hands-on software and platform engineering: building robust, well-tested, high-performance systems that scientists across the client nd on every day, on modern cloud technology and the vast biomedical data sources available to us.

In this role you will

• Design, build and operate scalable infrastructure and services that support our AI models and agentic systems across the entire software development life cycle.
• Own the reliability of what you build - set up CI/CD and release processes, automated testing, monitoring and alerting, and lead the response when things break, so the systems scientists rely on stay dependable.
• Build and operate the model-serving infrastructure that exposes our models in production with efficient use of compute.
• Develop and maintain cloud-native architectures that enable reliable deployment and scaling of AI/ML workloads.
• Deliver robust, tested and high-performance code in an agile environment, and work closely with ML engineers and domain experts to make the infrastructure fit for purpose.

Qualifications & Skills

• Demonstrated advanced programming expertise in Python and in developing and delivering robust, scalable software solutions using frameworks like FastAPI.
• Experience with cloud platforms (GCP, Azure) and cloud-native architectures.
• Passion for software design and commitment to the development of reusable, scalable, and testable software components.
• Basic understanding of at least one major deep learning framework (PyTorch, JAX, TensorFlow).
• Hands-on experience with Google Cloud Platform, in particular the services we build on: Cloud Run, Google Kubernetes Engine, Cloud Storage, Artifact Registry, Cloud SQL.
• Fluency in English.

Preferred Qualifications & Skills

• Familiarity with machine learning principles and state-of-the-art modelling approaches.
• Experience in design, development and deployment of commercial cloud-native software and infrastructure.
• Experience building and deploying large-scale AI models and agentic systems in production environments.
• Experience architecting, developing, and deploying distributed training pipelines for large models with PyTorch or TensorFlow.
• Expertise in performance optimization, cost optimization, and efficient compute resource management in cloud environments.
• Experience running production services at scale, including defining and working to service-level objectives (SLOs/SLIs).
• Experience with incident response and post-incident review, and with building the observability that supports it.
• Infrastructure-as-code (e.g. Terraform) for provisioning and maintaining cloud environments.
• Experience developing and administering workloads on Kubernetes (e.g. GKE).
• Familiarity with GCP networking and security controls - VPC, VPC Service Controls (VPC-SC), and private connectivity.
• Contributions to relevant open-source projects.
• Knowledge or interest in disease biology, molecular biology and medicine.
• Experience working with biomedical data (e.g., genomics, transcriptomics, proteomics, electronic health records, clinical images).