Interested in this AI/ML Engineer role at State Farm?
Apply Now →Skills & Technologies
About This Role
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. We're moving with urgency to become a Next Gen Good Neighbor, with technology that strengthens human connections and enhances the customer experience. As an employee, you'll spend less time navigating systems and more time applying judgment, empathy, and expertise in the moments that matter most. Position Location:
Bloomington, IL; Atlanta, GA; Dallas, TX; or Phoenix, AZ
Hybrid Work Arrangement:
In this position, you should plan to spend time working in the office each week and remotely (with the ability for same-day travel to and from your assigned office location) as part of a hybrid work arrangement. Work arrangements could change over time based on business need. Your manager will share additional information regarding your department's approach and what it means for you.
Incumbency: There is a 12-month incumbency period (beginning on the employee's effective date) for any lateral job movements and a 12-month incumbency period for any promotional opportunities, which must be met before the employee may post for other State Farm positions. The incumbency period does not affect the at-will relationship between State Farm and the employee and does not create an employment contract, nor contractual rights.
Applicants 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:
The State Farm P&C Actuarial & Underwriting Modeling (PCM) team is looking for an experienced NLP/Transformer Research Model Architect with deep expertise in transformer-based neural networks to join our innovative, dynamic, and fast-paced team of exceptional data science professionals. In this role, you'll apply cutting-edge NLP and deep learning research to real underwriting and pricing challenges — including modeling high-frequency time series data, extracting insight from unstructured text, and scaling transformer-based solutions efficiently within a resource-constrained production environment. You'll also play a key role in mentoring team members as they build design and experimentation skills in this space.
Key Responsibilities:
- Serves as a high-level consultant/researcher on department initiatives involving transformer-based neural networks and related deep learning architectures
- Designs, develops, fine-tunes, and deploys transformer models (e.g., attention mechanisms, encoder/decoder and encoder-only architectures, embeddings, transfer learning) for underwriting and pricing use cases, including modeling and forecasting on high-frequency time series data
- Designs and implements strategies to scale transformer-based solutions efficiently, optimizing for performance and cost within current compute and resource constraints
- Reviews and analyzes structured and unstructured data in innovative ways to refine underwriting, pricing, and risk selection objectives across the enterprise
- Identifies new data elements, criteria, or factors — including those derived from text, documents, high-frequency time series, and other unstructured or structured sources — for use in underwriting and pricing
- Conducts applied research studies in conjunction with other financial/research areas of the enterprise, staying current on state-of-the-art transformer and LLM research
- Mentors and trains other data scientists and modelers on the design, experimentation, and evaluation of transformer-based solutions, helping build internal capability in this space
- Consults with business clients on the use of modeling, machine learning, and AI, assessing needs and defining project scope
- Participates in external professional and industry committees/studies related to AI/ML and NLP
- Applies strong programming skills to build and productionize computer-intensive analysis techniques and model pipelines, with an eye toward scalability and efficient use of resources
- Routinely engages in quantitative analysis on non-standard and unique business problems
- Uses knowledge of business areas and research skills to address and solve a diverse range of real-world business issues
Qualifications: We Are Looking for Candidates With:
- 3-5 years of experience applying transformer-based neural networks and deep learning to real-world problems
- A Master's degree in Computer Science, Data Science, Statistics, Math, Actuarial Science, Information Technology, or a related field is required
- Hands-on experience with transformer architectures and techniques such as attention mechanisms, self-attention, embeddings, fine-tuning pretrained language models, and transfer learning
- Experience applying transformer-based or deep learning models to high-frequency time series data (e.g., forecasting, anomaly detection, or sequence modeling on densely sampled data)
- Experience scaling machine learning solutions in resource-constrained environments — e.g., model compression, distillation, quantization, efficient fine-tuning, or infrastructure/cost optimization
- Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch or TensorFlow, and libraries such as Hugging Face Transformers
- Experience applying NLP/deep learning techniques to structured and unstructured business data; insurance, financial services, or other regulated-industry experience is a plus
- Demonstrated experience mentoring or training other data scientists/engineers in model design and experimentation
- Strong communication skills and the ability to translate complex modeling concepts for non-technical business partners
Next Steps: Competitive candidates may be invited to participate in pre-employment on-demand video interviewing to kick off the interview process.
Applicants 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
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: $190,000-$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 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!
Salary Context
This $190K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At State Farm, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $190K to $220K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
State Farm AI Hiring
State Farm has 3 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span Bloomington, IL, US, Tempe, AZ. Compensation range: $200K - $220K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.