Interested in this AI/ML Engineer role at American Family Insurance?
Apply Now →About This Role
This position provides strategic leadership for applied AI, machine learning, and advanced analytics solutions that drive business performance and operational effectiveness. You will partner with business and technology leaders to define enterprise AI strategy and build the production\-grade capabilities, ensuring solutions are valuable, adopted, responsible, and aligned to enterprise priorities. This Director will also lead teams responsible for applied AI solution delivery, process\-level AI transformation, business\-domain partnership, model and solution quality, shared AI product lifecycle execution, portfolio delivery, and continuous improvement of AI\-enabled products and workflows. You will also work across Technology and business domains, including Information Security, Governance, Enterprise Architecture, Application Development, Digital Services, Infrastructure, Data Engineering, Product, Operations, Risk, Legal, and Compliance partners. The role also partners with external delivery, consulting, academic, and research partners.
Position Compensation Range:
$172,000\.00 \- $294,000\.00
Pay Rate Type:
Salary*Compensation may vary based on the job level and your geographic work location.* *Relocation support is offered for eligible candidates.*
Primary Accountabilities
- Lead applied AI solution delivery
+ You will direct the development and delivery of applied AI, machine learning, and advanced analytics solutions that address high\-value business problems.
+ Ensure solutions are production\-oriented, maintainable, measurable, and aligned to enterprise priorities.
- Advance process\-level AI transformation
+ You will partner with business and Technology leaders to identify, assess, and redesign high\-value workflows where AI can improve speed, quality, consistency, cost\-to\-serve, or customer/employee experience.
+ You will also assist teams connect individual AI capabilities into coherent business process journeys rather than isolated point solutions.
- Shape AI opportunities and portfolio alignment
+ Partner with business, Technology, product, portfolio, and operations leaders to identify and prioritize applied AI opportunities.
+ Remain in tune with frontier/evolving AI capabilities and emerging research in order to inform the approaching opportunities and long\-term strategy.
+ Translate ambiguous business needs into actionable AI solution opportunities, delivery roadmaps, and measurable outcomes.
- Lead applied science and solution quality
+ Provide leadership over applied modeling, AI solution design, prompt and agentic workflow patterns, experimentation design, validation, and solution evaluation.
+ You will ensure teams use appropriate scientific, statistical, engineering, and product practices for the problem being solved.
- Partner on AI product lifecycle management
+ You will work with business owners, product and portfolio leaders, AI Platform Engineering, and external delivery partners to shape AI product lifecycle practices from idea through production, adoption, continuous improvement, and retirement.
+ Clarify lifecycle responsibilities across business, Applied AI, AI Platform Engineering, and delivery partners so accountability is shared and sustainable.
- Drive value realization and adoption
+ Define success measures for AI solutions and ensure delivered capabilities are adopted, measured, iterated, and connected to business impact.
+ Help business partners move from AI awareness to operational use and sustained value.
- Develop reusable AI solution patterns
+ You will identify repeated solution patterns across domains and partner with AI Platform Engineering to convert them into reusable frameworks, components, and delivery accelerators.
+ Reduce one\-off builds where shared approaches can improve speed, quality, governance, and long\-term supportability.
- Ensure operational handoff and ongoing solution improvement
+ You will design applied AI solutions with supportability, escalation paths, and operational handoff in mind.
+ Partner with AI Platform Engineering and business/product owners on L3 enhancements, bug fixes, upgrades, and solution\-level improvements after production release.
- Embed responsible AI and evaluation discipline
+ Embed responsible AI practices into applied AI delivery in alignment with enterprise governance expectations.
+ Partner with business, governance, and platform teams to define domain\-specific evaluation criteria that reflect standards for quality, accuracy, compliance, fairness, customer experience, and business usefulness.
- Lead people and develop talent
+ Build and lead high\-performing teams of data scientists, applied AI practitioners, and related specialists aligned with enterprise strategy.
+ Set clear priorities, coach leaders and individual contributors, and create a collaborative, performance\-driven, inclusive environment.
Specialized Knowledge \& Skills Requirements
- Demonstrated experience delivering AI, machine learning, or advanced analytics solutions into production business workflows.
- Demonstrated ability to connect AI capabilities to measurable business outcomes and enterprise priorities.
- Demonstrated experience leading process\-level transformation, not only technology implementation.
- Strong understanding of applied AI, machine learning, advanced analytics, experimentation, validation, and production adoption.
- Experience with AI product lifecycle practices in a shared ownership model across business, data science, platform, product, portfolio, and external delivery partners.
- Demonstrated experience partnering across Information Security, Governance, Enterprise Architecture, Application Development, Digital Services, Infrastructure, Data Engineering, Product, Operations, Risk, Legal, Compliance, and business teams.
- Demonstrated ability to communicate complex technical concepts, tradeoffs, risks, and implications to non\-technical and executive audiences.
- Experience managing or partnering with external research, consulting, academic, or delivery partners.
- Demonstrated ability to lead through ambiguity, emerging technology, growth, and organizational change.
- Demonstrated effective people leadership, coaching, prioritization, and talent development skills.
Key Interfaces and Partners
- Business executives, business product owners, domain leaders, and operations teams.
- AI Platform Engineering for reusable technical patterns, MLOps/LLMOps, LLM pipeline frameworks, agentic workflow frameworks, deployment paths, observability, platform capabilities, production support patterns, and L3 enhancement partnership.
- BI Engineering \& Enablement for shared metrics, semantic context, BI consumption, trusted analytics foundations, and AI\-consumable business context.
- Data Engineering for data pipelines, curated data, AI\-ready data assets, and domain data foundations.
- Information Security, Enterprise Architecture, Infrastructure, Application Development, Digital Services, Cloud, Privacy, Legal, Compliance, Model Risk, AI Governance, and Data Governance.
- Product, program, and portfolio leaders for prioritization, operating model alignment, delivery orchestration, and value realization.
- External and contractor partners where they support applied AI delivery or operating model execution.
- UW\-Madison Data Science Institute and other external innovation or research partners
### Additional Information
- To ensure a strong start, all employees participate in our New Employee Orientation during their first week. This experience is held in person at our Madison, WI Headquarters or one of our AmFam core locations to help you connect with our mission, meet key team members and build relationships that support your growth. At times, sessions may be delivered virtually based on scheduling and availability.
- Offer to selected candidate will be made contingent on the results of applicable background checks
- Offer to selected candidate is contingent on signing a non\-disclosure agreement for proprietary information, trade secrets, and inventions
- Sponsorship will not be considered for this position unless specified in the posting
In this primarily office\-based role, you will be expected to spend at least 80% of your time (4\+ days per week) working from the office. Candidates should reside within approximately 35\-50 miles of one of the following office locations: Madison, WI 53783 or Boston, MA 02110\.
\#LI\- Onsite
We provide benefits that support your physical, emotional, and financial wellbeing. You will have access to comprehensive medical, dental, vision and wellbeing benefits that enable you to take care of your health. We also offer a competitive 401(k) contribution, a pension plan, an annual incentive, 9 paid holidays and a paid time off program (23 days accrued annually for full\-time employees). In addition, our student loan repayment program and paid\-family leave are available to support our employees and their families. Interns and contingent workers are not eligible for American Family Insurance Group benefits.
We are an equal opportunity employer. It is our policy to comply with all applicable federal, state and local laws pertaining to non\-discrimination, non\-harassment and equal opportunity. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.
American Family Insurance is committed to the full inclusion of all qualified individuals. If a reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please email [email protected] to request a reasonable accommodation.
\#LI\-AW1
Salary Context
This $172K-$294K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At American Family Insurance, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($233K) sits 7% above the category median. Disclosed range: $172K to $294K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
American Family Insurance AI Hiring
American Family Insurance has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Madison, WI, US. Compensation range: $294K - $294K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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