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. Come join a \#1 team and do some good!
Grow Your Skills, Grow Your Potential:
Responsibilities:
Join our team as a Property Field Inspection Claim Specialist and showcase your expertise in handling accident and weather\-related claims for homeowners, commercial properties, and large losses.
We are looking for an experienced and highly skilled professional to contribute to our dynamic team. You will be the first point of contact to meet with our insureds, explain coverage, estimate damages, and help them through the claims process while providing Remarkable service.
Key Responsibilities:* Conduct on\-site inspections and assessments of property damages for both residential and commercial claims
- Collaborate with policyholders, insurance agents, and other involved parties to gather information and resolve claims efficiently
- May occasionally require interacting with parties who express strong emotions or concerns about ongoing inspections or claim resolutions
- Provide exceptional customer service throughout the claims process, addressing inquiries and concerns promptly and professionally
- Gather necessary evidence, document findings, and prepare detailed reports to support the claims handling process
- Investigate and adjust both personal and commercial property claims with exposures up to $500,000
- Evaluate coverage and policy terms to determine the validity of claims and ensure compliance with local regulations
- Negotiate and settle claims within the authorized limits, considering policy provisions, industry standards, and company guidelines
Where you'll work: This position is located in Nanuet, NY. Competitive candidates should reside within one of the listed zip codes and will service this same territory:
10572 10901 10913 10920 10927 10931 10952 10954 10956 10960 10962 10964 10965 10968 10970 10974 10976 10977 10982 10983 10988 10989 10994 07430 07495 07495 07430 07458 07645 07656 07647 07620 07648 07675 07656 07446 10975 10910 10987 10917 10926 10911 10922 10996 10953 12518 12520
This is a Remote\-Field position in which you will work from home and utilize a mobile office/vehicle for in\-person appointments. Although the primary work location is in the field, with a commutable distance from home, there will be opportunities for virtual work to be completed at home. Additionally, there may be occasions where you will be required to travel outside your assigned area to assist in other territories.
Hours of operation are continually evaluated and may change based on business need. Successful candidates are able and willing to work flexible schedules and may be asked to work overtime and/or irregular hours.
Qualifications:
Competitive candidates must demonstrate:* Experience as a Property Field Inspection Claim Specialist in the insurance industry, specifically in property claims
- Strong knowledge of property insurance policies, coverage and claim handling practices
- Knowledge of both residential and commercial building construction
- Familiarity with local regulations and compliance requirements in your assigned territory
- Excellent communication and interpersonal skills to effectively interact with clients, agents, and other stakeholders
- Proven effective communication skills to handle difficult/emotional conversations with a customer\-minded focus
- Proven ability to assess damages, estimate repair costs, and negotiate settlements
- Detail\-oriented with strong organizational and analytical skills
- Proficient in using claims management software and other relevant tools
- Physical agility to allow for: frequent lifting, carrying and climbing a ladder; ability to navigate roofs at various heights for inspection of both residential and commercial structures; ability to crawl in tight spaces
- May be required to complete Rope and Harness Safety Training.
- A valid driver's license is required
Preferred:* Bachelor's Degree in a related field or equivalent work experience
- Experience in handling complex or high\-value claims
- Construction background
- Water mitigation inspection experience
- Xactimate, XactContents
Additional Details:* Employees must successfully complete all required training, including applicable licensing exam(s) and background checks required of various state(s).
- State Farm recently implemented new pre\-employment assessments. Candidates that have previously taken an assessment may be asked to participate in additional testing
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: $73,824\.56 \- $112,500\.00 annually
- + 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 $73K-$112K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($93K) sits 44% below the category median. Disclosed range: $73K to $112K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
State Farm AI Hiring
State Farm has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Nanuet, NY, US, Charleston, SC, US. Compensation range: $112K - $142K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.