Software Engineer*

Software Engineer x2 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. 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. 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. Demonstrated advanced programming expertise in Python and in developing and delivering robust, scalable software solutions using frameworks like FastAPI. Passion for software design and commitment to the development of reusable, scalable, and testable software components. Cloud Run, Google Kubernetes Engine, Cloud Storage, Artifact Registry, Cloud SQL. Fluency in English. 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 with incident response and post-incident review, and with building the observability that supports it. Familiarity with GCP networking and security controls - VPC, VPC Service Controls (VPC-SC), and private connectivity. Contributions to relevant open-source projects. Experience working with biomedical data (e.g., genomics, transcriptomics, proteomics, electronic health records, clinical images). If you receive suspicious outreach claiming to be from us, please contact us via the ManpowerGroup website.