Machine Learning Engineer, YuLife

• We are looking for a Machine Learning Engineer with strong data science roots and real-world deployment experience to help shape, optimise and scale our ML stack as we continue to grow
• 3+ years of hands-on experience in ML engineering, ideally having started in data science with a strong foundation in a quantitative field such as mathematics, statistics, physics, or computer science
• Strong Python programming skills with experience in building and maintaining production ML applications
• Demonstrated experience deploying ML models into production environments, including both batch and real-time/streaming contexts
• Proficiency working with distributed computing frameworks such as Apache Spark, Dask, or similar
• Experience with cloud-native ML deployment, particularly on AWS, using services like ECS, EKS, Fargate, Lambda, S3, and more
• Familiarity with orchestration and workflow scheduling tools such as Dagster, Airflow, or Prefect
• Knowledge of CI/CD best practices and tools (e.g. GitHub Actions, Jenkins, CodePipeline)
• Exposure to monitoring and observability tools for ML systems (e.g. Prometheus, Grafana, DataDog, WhyLabs, Evidently, etc.)
• Experience in building parallelised or distributed model inference pipelines

Desirable

• We’re looking for an experienced Machine Learning Engineer to join our growing data science team. This is a hands-on role for someone who has grown from a data science background and has transitioned into machine learning engineering
• You'll be working alongside our Lead Data Scientist and the wider data team to evolve and scale our ML infrastructure
• You’ll play a key role in building, deploying and optimising both real-time and batch ML models, ensuring they run smoothly and add measurable value to our product and users
• Designing, building, and deploying production-grade ML pipelines and services in Python
• Collaborating with data scientists and data engineers to productionise models and guide them through deployment best practices
• Improving the scalability, reliability, and automation of our ML infrastructure and workflows
• Developing systems for both real-time (low-latency) and batch inference use cases
• Working with AWS and other cloud services (e.g. Fargate, ECS, Lambda, S3, SageMaker, Step Functions) to deploy and monitor models in production
• Implementing and maintaining CI/CD pipelines for ML workflows, ensuring repeatability and robust version control
• Setting up monitoring and alerting frameworks to track model drift, data quality, and inference health
• Leveraging orchestration tools such as Dagster, Airflow, or Prefect to manage and scale ML workflows
• Supporting ongoing infrastructure migration or optimisation initiatives (e.g. improving cost efficiency, latency, or reliability)
• Partnering with product and engineering teams to ensure ML solutions are aligned with business goals, and that performance metrics and outcomes are clearly tracked
• Documenting and continuously evolving ML engineering best practices

• We usually respond within a week