Job description

Summary 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. Job Description 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. What You ll Bring Required 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. 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. Preferred Qualifications 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. Your Impact During your first six months, you will: Deliver core ML platform capabilities that improve the productivity of machine learning teams. Collaborate with cross-functional teams to build scalable, reliable, and secure ML infrastructure. Contribute to automation, operational excellence, and engineering best practices across the ML platform. Help improve the developer experience for building, deploying, and managing machine learning models. Support Guidewire s AI initiatives by delivering robust platform capabilities that enable teams to build and operate ML solutions efficiently. The US base salary range for this full-time position is $124,000 - $210,000. Your base pay will depend on your experience, skills, education, training, and location among other factors. All full-time positions or part-time roles working 30 hours or more a week at Guidewire are eligible for benefits that support their health and well-being including health, dental, and vision insurance, paid time off, and a company sponsored retirement plan. In addition, some roles may be eligible for the annual company bonus plan, commissions, and/or long term incentive awards which are contingent on a variety of factors including, but not limited to, company and employee performance. Disability Accommodations and Guidewire’s Appeals Process. Guidewire provides accommodations to the hiring process to create a fair opportunity for candidates with disabilities to contend for open positions. Accommodation requests should be directed to Accommodations guidewire.com. If things do not go as hoped, we invite you to use our appeals process. Guidewire promises to independently review any denied accommodation and any decision not to offer you the position. The appeals process is the same in either case. Within five business days of receiving a notice of denial of an accommodation, or receiving a notice of your non-selection for a vacancy, e-mail Accommodations guidewire.com to make an appeal. Guidewire will assign a new decision-maker to review the request and/or hiring decision, who will then notify you in writing of a decision within 10 business days. About Guidewire Guidewire is the platform P&C insurers trust to engage, innovate, and grow efficiently. We combine digital, core, analytics, and AI to deliver our platform as a cloud service. More than 540 insurers in 40 countries, from new ventures to the largest and most complex in the world, run on Guidewire. As a partner to our customers, we continually evolve to enable their success. We are proud of our unparalleled implementation track record with 1600 successful projects, supported by the largest R&D team and partner ecosystem in the industry. Our Marketplace provides hundreds of applications that accelerate integration, localization, and innovation. For more information, please visit www.guidewire.com and follow us on Twitter: Guidewire_PandC. Guidewire Software, Inc. is proud to be an equal opportunity and affirmative action employer. We are committed to an inclusive workplace, and believe that a diversity of perspectives, abilities, and cultures is a key to our success. Qualified applicants will receive consideration without regard to race, color, ancestry, religion, sex, national origin, citizenship, marital status, age, sexual orientation, gender identity, gender expression, veteran status, or disability. All offers are contingent upon passing a criminal history and other background checks where it s applicable to the position.

Requirements

Must-haves

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.
What You ll Bring Required 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.
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.
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.
Your Impact During your first six months, you will:
Deliver core ML platform capabilities that improve the productivity of machine learning teams.

Nice-to-haves

Preferred Qualifications Experience deploying and monitoring machine learning models in production.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learningaici/cdpythongojavadockerkubernetesawsazuregcp

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

Paid Time Off