ML Engineer

Being good neighbors – helping people, investing in our communities, and making the world a better place – is who we are at State Farm. It is at the core of how we operate and the reason for our success. Come join a #1 team and do some good!

HYBRID: Qualified candidates must live or relocate within a 180-mile radius of a hub location listed below and should plan to spend time working from home and some time working in the office as part of our hybrid work environment.
HUB LOCATIONS: Bloomington, IL;
Dunwoody, GA;
Richardson, TX; or Tempe, AZ

SPONSORSHIP:
Applicants for this position are required to be eligible to lawfully work in the U.S. immediately; employer will not sponsor applicants for U.S. work authorization (e.g. H-1B visa) for this opportunity

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

• 10+ 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 Lang Graph, Lang Chain, or similar…