Senior Machine Learning Platform Engineer

Posted 115ds ago

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Job Description

Senior Machine Learning Platform Engineer at Guidewire, architecting scalable ML platform solutions. Leading AI technology adoption and enabling data-driven innovation in the insurance software industry.

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.

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.

Benefits:

  • Flexible work environment
  • Health and wellness benefits
  • Paid time off programs, including volunteer time off
  • Market-competitive pay and incentive programs
  • Continual development and internal career growth opportunities