Interested in this AI/ML Engineer role at Information Technology Senior Management Forum?
Apply Now →Skills & Technologies
About This Role
Posted Date 10/15/2026 Description Overview
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
Responsibilities
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.
Qualifications
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 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: $150,000 - $198,000 (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 15% 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
- 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 LangGraph, LangChain, or similar agentic AI frameworks.
- Experience in insurance, financial services, or other regulated industries.
Salary150,000.00 - 198,000.00 Annual Type Full-time
Salary Context
This $150K-$198K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Information Technology Senior Management Forum, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($174K) sits 18% below the category median. Disclosed range: $150K to $198K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 31 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, AI Agent Developer. Positions span Charlotte, NC, US, McLean, VA, US, New York, NY, US. Compensation range: $167K - $392K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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.