Staff ML Engineer: Real-Time AI Systems & Decisioning

State Farm; Bloomington, Dunwoody, Richardson, Tempe (IL, GA, TX, AZ); Hybrid; USD 150,000 - 198,000; Full time

Benefits offered by this job

Annual raise

Bonus

Healthcare plans

Tuition assistance

Paid time off

Matching gifts

401(k) plan

Job summary

State Farm is seeking a hands-on Staff Machine Learning Engineer to architect, build, and scale enterprise-grade AI/ML systems. You will contribute to the NBA and recommendation platforms, focusing on ML systems engineering, productionization, and tool integration at scale.

You will lead ML pipelines with Python, PySpark, SQL, and Databricks on AWS, implement MLOps, and guide architectural decisions across teams while mentoring engineers.

Qualifications

• 10+ years of experience in ML engineering, software engineering, or related technical disciplines with leadership.
• Expert-level Python and strong SQL skills; APIs, microservices, and production software development.
• Expert-level PySpark and Apache Spark; building reliable data and feature pipelines.
• Deep hands-on Databricks expertise and strong AWS experience (S3, IAM, compute).
• Experience with full production ML lifecycle, MLflow and CI/CD.

Responsibilities

• Design and build scalable, production-grade ML systems, APIs, feature pipelines, and reusable components.
• Architect real-time or near-real-time decisioning and recommendation engines.
• Build feedback loops from customer interactions to improve recommendations.
• Develop data/ML pipelines using Python, PySpark, SQL, Databricks, and AWS.
• Implement MLOps, including deployment, monitoring, versioning, retraining, and CI/CD.
• Lead architecture decisions, establish engineering standards, mentor engineers.

Skills

Python

SQL

APIs & microservices

PySpark

Databricks

AWS

ML Engineering & MLOps

LLM orchestration

System architecture

Tools

Databricks

PySpark

Spark

AWS

MLflow

CI/CD

Job description

State Farm is seeking a hands-on Staff Machine Learning Engineer to architect, build, and scale enterprise-grade AI/ML systems. You will contribute to the NBA and recommendation platforms, focusing on ML systems engineering, productionization, and tool integration at scale.

You will lead ML pipelines with Python, PySpark, SQL, and Databricks on AWS, implement MLOps, and guide architectural decisions across teams while mentoring engineers.