Decisioning & Recommendation Engines: Architect real-time or near-real-time decisioning systems integrating predictive models, business rules, eligibility, prioritization, and optimization.
Closed-Loop Learning: Build feedback mechanisms that capture customer interactions and business outcomes to continuously improve recommendations.
Databricks & AWS Engineering: Develop reliable data and ML pipelines using Python, PySpark, SQL, Databricks, and AWS.
MLOps: Implement model deployment, serving, versioning, monitoring, CI/CD, and retraining workflows.
Agentic AI: Architect and develop production-grade agentic AI applications, including LLM orchestration, tool integration, evaluation, and guardrails.
Technical Leadership: Lead architecture decisions, establish engineering standards, mentor engineers, and collaborate across technical and business teams.
Qualifications
Required
8+ years of experience in ML engineering, software engineering, or related technical disciplines, with demonstrated technical leadership.
Programming: Expert-level Python and strong SQL skills; experience with APIs, microservices, and production software development.
Data Engineering: Expert-level PySpark and Apache Spark; experience building reliable data and feature pipelines.
Cloud & ML Platforms: Deep hands-on Databricks expertise (Delta Lake, Unity Catalog, Workflows) and strong AWS experience (S3, IAM, compute, and application integration).
ML Engineering & MLOps: Experience supporting the full production ML lifecycle, including model integration, deployment, serving, monitoring, versioning, and retraining. Familiarity with MLflow and CI/CD.
Real-Time Decisioning: Experience building low-latency inference services, decisioning APIs, and scalable recommendation systems.
Agentic AI: Hands-on experience developing LLM-powered or agentic AI applications, including orchestration, tool calling, and enterprise integrations.
System Architecture: Proven ability to independently architect and deliver complex, reliable enterprise systems.
Preferred
Hands-on experience developing or training ML models.
Experience building Next Best Action, recommendation, personalization, or decision optimization platforms.
Familiarity with contextual bandits, reinforcement learning, uplift modeling, or ranking algorithms.
Experience with LangGraph, LangChain, or similar agentic AI frameworks.
Experience in insurance, financial services, or other regulated industries.
Our Benefits
Because work-life balance is a priority at State Farm, compensation is based on our standard 38:45-hour work week!
Potential starting salary range: $160,000 - $220,000 (Starting salary will be based on skills, background, and experience. High end of the range limited to applicants with significant relevant experience.)
Potential yearly incentive pay up to 20% of base salary
At State Farm, we offer more than just a paycheck. Check out our suite of benefits designed to give you the flexibility you need to take care of you and your family!
Get Paid! On top of our competitive pay, you are eligible for an annual raise and bonus.
Stay Well! Focus on you and your family’s health with our robust health and wellbeing programs. State Farm pays most of your healthcare premium, and we offer multiple healthcare plan options, including a high deductible plan. All medical plans provide 100% coverage for in-network preventative care, AND you and your family have access to vision, dental, telemedicine, 24/7 mental health professionals, and much more!
Develop and Grow! Take advantage of educational benefits like industry leading training programs, top-notch tuition assistance programs, employee resource groups, and mentoring.
Plan Ahead! Plan for those big moments in life with benefits like fertility/IVF/adoption assistance, college coaching, national discount programs, interactive monthly financial workshops, free financial coaching, and more. You can also start a savings account or consider financing through our State Farm Federal Credit Union!
Take a Little “You” Time! You will have access to our generous time off policies designed so you can plan around holidays, family events, volunteering, or just to take a relaxing day off. With the opportunity to initially earn up to 20 days annually plus parental leave, paid holidays, celebration day, life leave (40 hours/year), bereavement leave, and community service/education support days, there will be plenty of time for you!
Give Back! We offer several ways to give back through our Matching Gift Program, Good Neighbor Grant Program, and the Employee Assistance Fund.
Finish Strong! Plan for retirement using free financial advisors and a 401(k) plan with company contributions of up to 7% of your salary.
Visit our State Farm Careers page for more information on our benefits, locations, and the hiring process of joining the State Farm team!
We are seeking a hands-on Staff Machine Learning Engineer to architect, build, and scale enterprise-grade AI/ML systems, real-time decisioning capabilities, and agentic AI applications.
This role will help develop our next-generation Next Best Action (NBA) and recommendation platform, leveraging Databricks, AWS, and modern AI/ML technologies. The focus is on ML systems engineering, architecture, and productionization rather than developing predictive models from scratch.
ML Architecture & Engineering: Design and build scalable, production-grade ML systems, APIs, feature pipelines, and reusable engineering components.
Decisioning & Recommendation Engines: Architect real-time or near-real-time decisioning systems integrating predictive models, business rules, eligibility, prioritization, and optimization.
Closed-Loop Learning: Build feedback mechanisms that capture customer interactions and business outcomes to continuously improve recommendations.
Databricks & AWS Engineering: Develop reliable data and ML pipelines using Python, PySpark, SQL, Databricks, and AWS.
MLOps: Implement model deployment, serving, versioning, monitoring, CI/CD, and retraining workflows.
Agentic AI: Architect and develop production-grade agentic AI applications, including LLM orchestration, tool integration, evaluation, and guardrails.
Technical Leadership: Lead architecture decisions, establish engineering standards, mentor engineers, and collaborate across technical and business teams.
Required
8+ years of experience in ML engineering, software engineering, or related technical disciplines, with demonstrated technical leadership.
Programming: Expert-level Python and strong SQL skills; experience with APIs, microservices, and production software development.
Data Engineering: Expert-level PySpark and Apache Spark; experience building reliable data and feature pipelines.
Cloud & ML Platforms: Deep hands-on Databricks expertise (Delta Lake, Unity Catalog, Workflows) and strong AWS experience (S3, IAM, compute, and application integration).
ML Engineering & MLOps: Experience supporting the full production ML lifecycle, including model integration, deployment, serving, monitoring, versioning, and retraining. Familiarity with MLflow and CI/CD.
Real-Time Decisioning: Experience building low-latency inference services, decisioning APIs, and scalable recommendation systems.
Agentic AI: Hands-on experience developing LLM-powered or agentic AI applications, including orchestration, tool calling, and enterprise integrations.
System Architecture: Proven ability to independently architect and deliver complex, reliable enterprise systems.
Preferred
Hands-on experience developing or training ML models.
Experience building Next Best Action, recommendation, personalization, or decision optimization platforms.
Familiarity with contextual bandits, reinforcement learning, uplift modeling, or ranking algorithms.
Experience with LangGraph, LangChain, or similar agentic AI frameworks.
Experience in insurance, financial services, or other regulated industries.
