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About This Role
Posted Date 9/23/2026 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!
REMOTE:Qualified candidates living outside a 50-mile radius of a hub location listed below can plan to work remote.
HYBRID: Qualified candidates living within a 50-mile radius of a hub location listed below 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.
Grow Your Skills, Grow Your Potential
Responsibilities
At the State Farm Applied Innovation Lab, we’re building an Agentic SDLC control plane, where GenAI helps with every part of how we build, test, deploy, and run software. We’re looking for someone who has run real workloads on Databricks in production and knows Delta Lake, Unity Catalog, and MLflow inside and out. Just as important, they already work the way we do, reaching for agentic tools naturally whether they’re building something new or tracking down a bug. If you’re happiest at the command line, you’ll feel right at home here.
In this role, you’ll own the performance, quality, and production-readiness of select innovation projects running on Databricks – the ones where we dig deep to learn what excellence really looks like, then turn those lessons into patterns we can reuse on future projects. Other teams take care of the platform and infrastructure; you’ll own the layer above it: the data foundations, governance, FinOps, lineage, runbooks, and the handoff to Enterprise Technology. When a project leaves our hands, it’s fully governed, tagged at the project level, and optimized for cost, with a low L3 incident rate, complete runbooks, and self-healing wherever possible.
This is a lead individual contributor role: you’ll set technical direction and raise the bar across teams without managing people. You’ll partner with teams across the enterprise, take on hard problems on Databricks, and help shape how we put the platform’s newest capabilities to work on the goals that matter most to State Farm. It’s a great fit if you want to grow your skills at the leading edge of Databricks and agentic AI, work on high-visibility initiatives, and do your best work in a fast-moving, experimental setting.
What You’ll Own
Data Foundations – Design and run medallion architecture (Bronze Silver* Gold) with solid data quality, schema management, and Delta Lake tuning. Everything downstream depends on getting this right.
- Application Performance – Hunt down bottlenecks in Spark queries, partitioning, caching, and Databricks cluster sizing, then fix them and keep them from coming back.
- Unity Catalog & Lineage – Own schemas, access controls, and project-level tagging, with lineage-driven ticketing so upstream breaks surface right away.
- MLOps / MLflow 3.0 – Take models through their full lifecycle, from experiment to production to retirement, including retraining and drift detection.
- FinOps – Make Databricks cost visible by default: track, alert on, and optimize spend for every project.
- ET Handoff – Define and hold the line on “done”: low L3 incident rate, complete runbooks, self-healing where possible, and governance and FinOps buttoned up. When a project crosses the handoff plane, it’s ready.
- Patterns & Practices – Turn what you learn into templates and standards that make every future project better.
- Agentic Integration – Bring agentic AI into everything you do: data validation, pipeline generation, testing, monitoring, documentation. If it can be augmented, it should be.
- Technical Leadership & Collaboration – Set technical direction, mentor engineers, and pair with pod teams on architecture, code reviews, and debugging. You teach by doing.
Qualifications
- Databricks (Non-Negotiable) – At least a year running Databricks in production at the application layer. You’ve designed and operated medallion architecture, and you know Delta Lake, Unity Catalog, and PySpark cold.
- Production ML (Non-Negotiable) – You’ve kept models healthy in production, not just trained them and handed them off. You’ve been paged, written the RCA, and caught drift and silent degradation before they became someone else’s problem.
- Agentic-Native (Non-Negotiable) – GenAI and agentic tools are already part of how you work every day, not an experiment. You’re at home on the command line and always looking for the next thing to automate.
- MLflow 3.0 – Hands-on with the full lifecycle: registry, experiment tracking, deployment automation, retraining pipelines, and drift detection.
- FinOps – You’ve tracked and optimized Databricks spend at the project level, and you treat compute budgets with the same rigor as SLAs.
- Data Lineage & Observability – You’ve built or run systems that track lineage end to end and open tickets automatically on upstream breaks, schema changes, or SLA misses.
- Production Handoff – You’ve prepared systems for operations teams with runbooks, self-healing patterns, incident targets, and governance checklists.
- Systems Thinking – You see the whole system, not just the model or the pipeline. You’ve read Hidden Technical Debt in Machine Learning Systems, and you’ve lived it.
- Engineering – Production-grade Python, PySpark, SQL, and AWS; CI/CD for data and ML pipelines; and testing strategies for non-deterministic systems.
- Omnigent (Nice to Have) – Bonus points if you’ve worked with Omnigent, Databricks’ open-source agent meta-harness
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: $100,000 - $200,000. Starting salary will be based on skills, background, and experience. High end of the range limited to applicants with significant relevant experience and living in high cost of labor location like CA/NY/NJ.
- 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!
At the State Farm Applied Innovation Lab, we’re building an Agentic SDLC control plane, where GenAI helps with every part of how we build, test, deploy, and run software. We’re looking for someone who has run real workloads on Databricks in production and knows Delta Lake, Unity Catalog, and MLflow inside and out. Just as important, they already work the way we do, reaching for agentic tools naturally whether they’re building something new or tracking down a bug. If you’re happiest at the command line, you’ll feel right at home here.
In this role, you’ll own the performance, quality, and production-readiness of select innovation projects running on Databricks – the ones where we dig deep to learn what excellence really looks like, then turn those lessons into patterns we can reuse on future projects. Other teams take care of the platform and infrastructure; you’ll own the layer above it: the data foundations, governance, FinOps, lineage, runbooks, and the handoff to Enterprise Technology. When a project leaves our hands, it’s fully governed, tagged at the project level, and optimized for cost, with a low L3 incident rate, complete runbooks, and self-healing wherever possible.
This is a lead individual contributor role: you’ll set technical direction and raise the bar across teams without managing people. You’ll partner with teams across the enterprise, take on hard problems on Databricks, and help shape how we put the platform’s newest capabilities to work on the goals that matter most to State Farm. It’s a great fit if you want to grow your skills at the leading edge of Databricks and agentic AI, work on high-visibility initiatives, and do your best work in a fast-moving, experimental setting.
What You’ll Own
Data Foundations – Design and run medallion architecture (Bronze Silver* Gold) with solid data quality, schema management, and Delta Lake tuning. Everything downstream depends on getting this right.
- Application Performance – Hunt down bottlenecks in Spark queries, partitioning, caching, and Databricks cluster sizing, then fix them and keep them from coming back.
- Unity Catalog & Lineage – Own schemas, access controls, and project-level tagging, with lineage-driven ticketing so upstream breaks surface right away.
- MLOps / MLflow 3.0 – Take models through their full lifecycle, from experiment to production to retirement, including retraining and drift detection.
- FinOps – Make Databricks cost visible by default: track, alert on, and optimize spend for every project.
- ET Handoff – Define and hold the line on “done”: low L3 incident rate, complete runbooks, self-healing where possible, and governance and FinOps buttoned up. When a project crosses the handoff plane, it’s ready.
- Patterns & Practices – Turn what you learn into templates and standards that make every future project better.
- Agentic Integration – Bring agentic AI into everything you do: data validation, pipeline generation, testing, monitoring, documentation. If it can be augmented, it should be.
- Technical Leadership & Collaboration – Set technical direction, mentor engineers, and pair with pod teams on architecture, code reviews, and debugging. You teach by doing.
- Databricks (Non-Negotiable) – At least a year running Databricks in production at the application layer. You’ve designed and operated medallion architecture, and you know Delta Lake, Unity Catalog, and PySpark cold.
- Production ML (Non-Negotiable) – You’ve kept models healthy in production, not just trained them and handed them off. You’ve been paged, written the RCA, and caught drift and silent degradation before they became someone else’s problem.
- Agentic-Native (Non-Negotiable) – GenAI and agentic tools are already part of how you work every day, not an experiment. You’re at home on the command line and always looking for the next thing to automate.
- MLflow 3.0 – Hands-on with the full lifecycle: registry, experiment tracking, deployment automation, retraining pipelines, and drift detection.
- FinOps – You’ve tracked and optimized Databricks spend at the project level, and you treat compute budgets with the same rigor as SLAs.
- Data Lineage & Observability – You’ve built or run systems that track lineage end to end and open tickets automatically on upstream breaks, schema changes, or SLA misses.
- Production Handoff – You’ve prepared systems for operations teams with runbooks, self-healing patterns, incident targets, and governance checklists.
- Systems Thinking – You see the whole system, not just the model or the pipeline. You’ve read Hidden Technical Debt in Machine Learning Systems, and you’ve lived it.
- Engineering – Production-grade Python, PySpark, SQL, and AWS; CI/CD for data and ML pipelines; and testing strategies for non-deterministic systems.
- Omnigent (Nice to Have) – Bonus points if you’ve worked with Omnigent, Databricks’ open-source agent meta-harness
Salary100,000.00 - 200,000.00 Annual Type Full-time
Salary Context
This $100K-$200K range is above the median for MLOps Engineer roles in our dataset (median: $157K across 20 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 9,653 AI roles we're tracking, MLOps Engineer positions make up 2% of the market. At Information Technology Senior Management Forum, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $205,000 based on 151 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($150K) sits 27% below the category median. Disclosed range: $100K to $200K.
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.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 30 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, MLOps Engineer, AI Safety. Positions span New York, NY, US, McLean, VA, US, Riverwoods, IL, US. Compensation range: $168K - $434K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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.
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