Senior Machine Learning Platform Engineer

Guidewire Software

1d•$148,000 - $222,000•Hybrid

About The Position

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. 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. 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.

Requirements

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.

Nice To Haves

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).

Responsibilities

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.