Interested in this MLOps Engineer role at Guidewire?
Apply Now →Skills & Technologies
About This Role
United States \- San Mateo, CA
Product Development and Operations/Full time/Hybrid
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 AI/ML Platform Engineer, you will architect and scale the ML platform for data scientists and ML engineers 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
High\-priority opening — we’re moving fast and looking to hire ASAP.
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).
Advanced experience 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).
Your Impact
We believe in clarity and setting you up for success. In your first six months, you’ll lead the design and implementation of core ML platform components, collaborate with cross\-functional teams to deliver scalable solutions, and establish best practices for ML operations. Your work will directly support Guidewire’s mission to deliver secure, efficient, and innovative insurance technology, driving measurable value for our customers and accelerating the adoption of AI and cloud capabilities. Over time, your leadership will influence the technical direction of our ML platform and empower teams across the company.
What’s in it for you
The people we employ give their all, and in return, we offer flexibility wherever we can, such as:
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
A new in\-person orientation process for all roles
At Guidewire, you’ll help transform the insurance industry, working alongside a collaborative, innovative team committed to customer success and continuous improvement. Your contributions will support our wider mission to deliver measurable value, efficiency, and success for customers through secure, scalable, and AI\-powered solutions.
The US base salary range for this full\-time position is $148,000 \- $247,000\. 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 [email protected]. 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 [email protected] 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.
Interested in this position?
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.
Salary Context
This $148K-$247K range is below the median for MLOps Engineer roles in our dataset (median: $209K across 26 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,824 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Guidewire, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $217,200 based on 76 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($197K) sits 9% below the category median. Disclosed range: $148K to $247K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Guidewire AI Hiring
Guidewire has 2 open AI roles right now. They're hiring across AI Architect, MLOps Engineer. Positions span Remote, US, San Mateo, CA, US. Compensation range: $198K - $247K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.