Senior Machine Learning Platform Engineer

Summary

Join Guidewire’s Product Development & Operations (PDO) team, where we deliver operational excellence and transformative innovation for the world’s leading P&C insurance software. Our team is at the forefront of AI, cloud, and data platform adoption, working collaboratively in a hybrid environment to ensure secure, scalable, and efficient solutions. We thrive on curiosity, continuous improvement, and a culture that values diverse perspectives and teamwork. ¹

As a Senior Machine Learning Platform Engineer, you will architect and scale the ML platform that powers Guidewire’s next-generation products. This is a high-impact role for a technical leader passionate about distributed systems, MLOps, and empowering data-driven innovation. You will help shape the future of insurance technology by enabling seamless ML workflows and accelerating the adoption of AI across Guidewire’s solutions.

Job Description

What you’ll do

• Architect and guide the design of a scalable, secure ML platform supporting the full ML lifecycle, from data ingestion to model monitoring.

• Design and implement infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry.

• Orchestrate ML workflows using tools such as Kubeflow, SageMaker, MLflow, or similar.

• Collaborate with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering to define best practices for reproducibility, governance, and CI/CD for ML.

• Partner with Data Engineers to build robust data pipelines for model-ready datasets.

• Optimize ML workload performance across compute and storage layers using cloud-native and open-source solutions.

• Lead technical discussions, mentor junior engineers, and help set the technical vision for the ML platform roadmap.

• Ensure compliance with security, privacy, and regulatory requirements throughout the ML lifecycle.

• At Guidewire, we foster a culture of curiosity, innovation, and responsible use of AI—empowering our teams to continuously leverage emerging technologies and data-driven insights to enhance productivity and outcomes.

What you’ll bring

Required

• Demonstrated ability to embrace AI and apply it to your current role as well as data-driven insights to drive innovation, productivity, and continuous improvement.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• 10+ years of software engineering experience, including 5+ years working on ML platforms or infrastructure.

• Expertise in building large-scale distributed systems and microservices.

• Strong programming skills in Python, Go, or Java.

• Experience with containerization and orchestration (e.g., Docker, Kubernetes).

• Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks.

• Cloud platform experience (AWS, GCP, or Azure).

• Experience with statistical learning algorithms (GLM, XGBoost, Random Forest) and deep learning (neural networks, transformers).

• Strong communication, leadership, and problem-solving skills.

Preferred

• Experience with real-time model inference and streaming ML pipelines.

• Deep knowledge of model governance, reproducibility, and monitoring.

• Understanding of model performance metrics and drift detection.

• Exposure to feature stores (Feast, Tecton) and workflow tools (Airflow, Argo).

• Familiarity with regulatory considerations (model auditability, interpretability, data privacy laws such as CCPA/GDPR).

• Experience with real-time data pipelines (Kafka, Flink, Spark Structured Streaming).

• Experience using TeamCity and Terraform for infrastructure setup and CI/CD.

• Insurance industry or related experience (banking, finance).<