Hiring: ML Platform Engineer Company: Guidewire Software Location: San Mateo, CA Job Posted Time: 2026-09-10 11:56:41 Employment Type: Full-time Target Skills & Keywords : AWS, Airflow, Azure, CI/CD, Databricks, Docker, Feature Store, GCP, Infrastructure as Code, Java, Kafka, Kubeflow, Kubernetes, MLOps, MLflow, Machine Learning, Model Registry, Python, R, SageMaker, Spark, Terraform, Vertex AI About the job Experience: •3+ years of software engineering experience, including experience building or supporting ML platforms, data platforms, or cloud-native applications. Required Skills: •Design, develop, and maintain components of a scalable and secure ML platform supporting the machine learning lifecycle, from data ingestion and model training to deployment and monitoring. •Build infrastructure for model training, experiment tracking, hyperparameter tuning, and model registry using tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or similar technologies. •Develop and maintain automated ML workflows and CI/CD pipelines for machine learning applications. •Partner cross-functionally with Data Scientists and Data Engineers to build reliable, model-ready datasets and improve the ML development experience. •Help optimize ML workloads across cloud infrastructure, compute, and storage to improve scalability and efficiency. •Contribute to platform reliability by implementing monitoring, logging, testing, and operational best practices. •Participate in design discussions, code reviews, and technical planning while contributing to engineering best practices. •Ensure platform components meet security, privacy, and compliance requirements. Qualifications: •Demonstrated ability to embrace AI and apply it in day-to-day engineering work to improve productivity and software quality. •Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. •Strong programming skills in Python, Go, or Java. •Operational familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks. •Basic understanding of machine learning workflows and common algorithms. •Strong communication, collaboration, and problem-solving skills. •Operational familiarity with feature stores, workflow orchestration tools (Airflow, Argo), or model monitoring solutions. •Exposure to streaming technologies such as Kafka or Spark. •Operational familiarity with ML governance, reproducibility, and model lifecycle management. Compensation: •$124,000 - $210,000 / year Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!