Staff/Senior Machine Learning Platform Engineer

Summary

We are seeking a seasoned Staff/Senior Machine Learning Platform Engineer to lead the design, development, and scaling of our end-to-end ML platform. You will play a critical role in empowering data scientists and ML engineers to build, train, deploy, and monitor machine learning models at scale. The role requires deep expertise in distributed systems, infrastructure automation, and machine learning workflows.

You will work closely with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering teams in abstracting away the complexities, ensuring performance, and accelerating innovation throughout our ML initiatives.

Job Description

Responsibilities

• Architect and guide the design of a scalable and secure ML platform to support the entire ML lifecycle (data ingestion, feature engineering, model training, deployment, and monitoring).

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

• Orchestrate ML workflows using tools like Kubeflow, SageMaker, MLflow, or similar orchestration tools.

• Collaborate with cross-functional teams to define best practices for reproducible research, model versioning, governance, and CI/CD for ML.

• Collaborate with Data Engineers to facilitate building Data Pipelines for model ready datasets.

• Optimize performance of ML workloads 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 across the ML lifecycle.

Required Qualification

• 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.

• Experience 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).

• Understanding of MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks

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

• Experience using statistical learning algorithms such as GLM, XGBoost, and Random Forest to solve real world business problems.

• Deep understanding of neural network and transformer algorithms

Preferred Qualifications

• Real-time model inference and streaming ML pipelines experience.

• 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 working 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 such as banking and finance

What We Offer

• A chance to influence and build the ML platform used across the company.

• Collaborative and diverse team environment with opportunities to mentor and be mentored.

• Competitive compensation, equity, and benefits package.

• Support for continuous learning and development.
   

The US base salary range for this full-time position is $144,000 - $240,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

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.