ML Platform Engineer

Guidewire•San Mateo, CA

2d•Onsite

About The Position

As an ML Platform Engineer, you'll help build and evolve the infrastructure that enables machine learning teams to develop, deploy, and operate models efficiently at scale. You'll work closely with Data Scientists, Data Engineers, MLOps engineers, and Product Engineering teams to build reliable, secure, and scalable ML platform capabilities. What you'll do Key responsibilities include: 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. Collaborate 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. At Guidewire, we foster a culture of curiosity, innovation, and responsible AI. We encourage engineers to leverage emerging AI capabilities and data-driven insights to improve engineering productivity and deliver secure, scalable solutions for the insurance industry.

Requirements

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.
3+ years of software engineering experience, including experience building or supporting ML platforms, data platforms, or cloud-native applications.
Strong programming skills in Python, Go, or Java.
Experience with Docker and Kubernetes or similar container orchestration technologies.
Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks.
Experience working with cloud platforms such as AWS, Azure, or GCP.
Basic understanding of machine learning workflows and common algorithms.
Strong communication, collaboration, and problem-solving skills.

Nice To Haves

Experience deploying and monitoring machine learning models in production.
Familiarity with feature stores, workflow orchestration tools (Airflow, Argo), or model monitoring solutions.
Exposure to streaming technologies such as Kafka or Spark.
Experience with Infrastructure as Code and CI/CD tools such as Terraform and TeamCity.
Familiarity with ML governance, reproducibility, and model lifecycle management.
Experience in the insurance, financial services, or another regulated industry.

Responsibilities

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.
Collaborate 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.