Software Engineer

Software Engineer (Platform & DevOps) About the Role We are looking for a pragmatic Software Engineer to help build and scale our computational drug discovery platform. You will develop the core platform, automate infrastructure, and ensure our workflows are reproducible, reliable, and production-ready. This is a hands-on engineering role that spans backend development, cloud infrastructure, workflow orchestration, and DevOps. You will work closely with computational chemists, machine learning engineers, and scientists to turn research workflows into scalable production systems. Responsibilities Design, develop, and maintain the core platform and backend services.Build and maintain cloud-native infrastructure on AWS using Infrastructure as Code.Develop, deploy, and monitor containerized applications using Docker and Kubernetes/ECS where appropriate.Build and maintain CI/CD pipelines for reliable software delivery.Design workflow orchestration systems (e.g. Prefect) for large-scale computational pipelines.Improve platform reliability, observability, monitoring, and logging.Optimize compute infrastructure for high-performance workloads, including GPU resources.Work with engineering and scientific teams to productionize new computational methods.Improve developer experience through tooling, automation, documentation, and internal libraries.Ensure platform security, scalability, and operational excellence. Required Qualifications Strong software engineering experience with Python.Experience building backend services and APIs.Experience with AWS and cloud-native application development.Strong understanding of Docker and containerised deployments.Experience with CI/CD tools such as GitHub Actions.Experience with Infrastructure as Code (Terraform, AWS CDK or similar).Familiarity with Linux systems and shell scripting.Experience with relational databases and object storage.Strong debugging and problem-solving skills.Excellent communication skills and the ability to work in multidisciplinary teams. Nice to Have Experience with workflow orchestration tools such as Prefect, Airflow, or Dagster.Experience with Kubernetes, AWS Batch, ECS, or Slurm.Experience managing GPU infrastructure.Experience with MLOps tools such as MLflow.Experience building scientific computing or data platforms.Knowledge of computational chemistry, molecular modelling, or bioinformatics.Experience with distributed computing and large-scale data processing.Experience with LLMs, MCP, and agentic systems. What You'll Build Scalable computational workflows.Cloud infrastructure for large-scale scientific computing.Internal platform services and developer tooling.Automated deployment pipelines.Monitoring, logging, and operational tooling.Infrastructure that enables researchers to run complex computational workflows with minimal operational overhead. What We're Looking For We value engineers who enjoy owning systems end-to-end. The ideal candidate is comfortable moving between writing production-quality code, automating infrastructure, debugging distributed systems, and improving platform reliability. You should enjoy solving practical engineering problems and working closely with scientific teams to translate research into scalable software. Technologies Typical technologies include: PythonAWS (Batch, ECS, EC2, S3, IAM, CloudWatch)DockerPrefectGitHub ActionsTerraform/CDKPostgreSQLLinuxRedis (optional)Kubernetes (nice to have)MLflow (nice to have)Prometheus/Grafana/OpenTelemetry (nice to have)