Interested in this AI/ML Engineer role at Polaris Industries?
Apply Now →About This Role
At Polaris Inc., we have fun doing what we love by driving change and innovation. We empower employees to take on challenging assignments and roles with an elevated level of responsibility in our agile working environment. Our people make us who we are, and we create incredible products and experiences that empower us to THINK OUTSIDE.
*Track:* *Predictive Data Science*
*This track is designed for individuals who want to apply data science,* *advanced analytics**, and* *emerging technologies* *to solve business challenges, uncover insights, and help drive smarter decisions across Polaris.*
Leadership Development Program Overview
The Polaris Digital \& Information Technology (D\&IT) Leadership Development Program is a two\-year rotational program designed for recent graduates interested in technology, business, and leadership. Through four six\-month rotations, associates gain hands\-on experience applying data, analytics, machine learning, and AI to solve real business challenges across Polaris.
The Predictive Data Science Track provides exposure to the full data science lifecycle — from identifying business problems and preparing data to building predictive models, deploying machine learning solutions, and translating insights into action. Associates work alongside data scientists, data engineers, product teams, business stakeholders, and technology leaders to develop solutions that improve customer experiences, optimize operations, and support innovation across the enterprise.
This track is ideal for individuals who are analytical, curious, and energized by using data to solve complex problems. Throughout the program, associates build technical, business, and leadership skills while gaining broad exposure to how data science and advanced analytics drive business outcomes across Polaris.
As a Predictive Data Science ITDP, you may gain experience in:
- Business Analytics \& Decision Support – Analyzing business, customer, product, and operational data to uncover insights and deliver recommendations that create measurable business value.
- Connected Product \& Data Insights – Leveraging connected product and business data to uncover insights, improve experiences, and support data\-driven decision\-making.
- Predictive Modeling \& Machine Learning – Building, training, and evaluating models that support forecasting, optimization, personalization, and operational decision\-making.
- AI \& Emerging Technologies – Exploring opportunities in generative AI, agentic AI, large language models, deep learning, and other emerging technologies to support business innovation.
- MLOps \& Model Deployment – Supporting scalable machine learning pipelines, model deployment processes, monitoring, automation, and continuous improvement practices.
- Cross\-Functional Data Science Leadership – Collaborating with business partners, engineers, product teams, and technology leaders to translate business needs into data science solutions.
Program Length: 2 Years
Rotation Length: 6 Months, 4 Rotations
Placement Locations May Include: Many ITDPs are based out of our corporate headquarters in Medina, MN, but opportunities may also exist in Plymouth, MN; Wyoming, MN; Roseau, MN; Huntsville, AL; Wilmington, OH; and Vermillion, SD.
Upon completion of the program, you will transition into a full\-time D\&IT\-related position within the business, contingent on business needs and the skills and experience you demonstrate during the program.
Program Advantages:As a Polaris employee, you will enjoy specific benefits beyond rotational experiences, including:
- Mentorship: you are paired with one of our functional business leaders for mentor support throughout your entire DP journey.
- Development: a blend of structured and self\-guided learning opportunities provided throughout the program that are intended to build leadership skills.
- Networking: intentional time with peers and leaders at Polaris to build your professional network.
- Early Talent Summit Week*:* you participate in our 3\-day event in our Wyoming, MN office. This immersive experience brings together all US interns and DPs for professional development, structured networking, a DP graduation event, inspiring executive speakers, and a thrilling team ride on some of your favorite Polaris vehicles.
- Community Engagement: opportunity to get involved in your local community.
- End of Rotation Presentations: present to our senior level leaders to showcase your career aspirations and recap your rotational experience.
Polaris Benefit Highlights:
- A generous 401K employee contribution matching program.
- Pay for Performance Company which uniquely allows employees to receive Annual Profit\-Sharing bonuses based upon the performance of the employee.
- Tuition Reimbursement program to support employees who want to further their education.
The Selection Process:
Applications are reviewed on a rolling basis throughout the fall. Selected candidates will participate in a phone screen, business screen, and virtual interview with Polaris HR and business leaders. Candidates who successfully complete the interview process may receive an offer to join the D\&IT Leadership Development Program. Rotation placements are determined based on business needs, individual interests, and development goals, with start dates aligned to graduation timing in either January or June.
DP Relocation Assistance:
Polaris offers a relocation program through our mobility vendor for employees who qualify. Benefits include a lump sum payment, self\-haul moving package, lease cancellation/duplicate housing reimbursement, and access to a relocation counselor. The counselor will explain available resources and assist in securing short\-term housing. You will be responsible for daily transportation to and from the office.
Required Qualifications
- Bachelor's or Master's degree in Data Science or related field with graduation date between August 2026 and May 2027\.
- Minimum overall GPA of 3\.0\.
- Ability to start January 11, 2027 or June 14, 2027\.
- U.S. work authorization without future sponsorship requirements.
- Reliable transportation.
- Willingness to travel and relocate throughout the rotational program.
- Proficiency in Microsoft Office (Outlook, Excel, Word, Teams, SharePoint).
- Demonstrated leadership experience through student organizations and/or work experiences.
- Demonstrated strengths in analysis, creative problem\-solving, communication, interpersonal relationships, self\-motivation, and leadership.
Preferred Qualifications
- Previous internship, co\-op, research, or project experience involving data analytics, data science, machine learning, artificial intelligence, or statistical modeling.
- Interest in applying data, analytics, and AI to solve business challenges and drive decision\-making.
- Experience working with data to identify trends, develop insights, or support recommendations.
- Familiarity with data science, machine learning, predictive analytics, artificial intelligence, or data visualization concepts.
- Ability to communicate data\-driven insights and recommendations to technical and non\-technical audiences.
- Demonstrated curiosity and a passion for learning new technologies and analytical techniques.
*This position is not eligible for sponsorship. To be considered for this opportunity, you must apply on our career page.* *We* *hope you're ready* *for the ultimate adventure!*
The starting pay range for Minnesota is $79,000 to $88,500 per year. Individual salaries and positioning within the range are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills, and geography. While individual pay could fall anywhere in the range based on these factors, it is not common to start at the high end or top of the range.
To qualify for this position, former employees must be eligible for rehire, and current employees must be in good standing.
*We are an ambitious, resourceful, and driven workforce, which empowers us to THINK OUTSIDE.**Apply today!*
At Polaris we put our employees first, by offering a holistic approach to their health and financial wellbeing. Polaris is proud to offer competitive compensation, including a market\-leading profit\-sharing plan that is fundamental to our pay\-for\-performance culture. At Polaris, employees are owners of the company through company contributions to our Employee Stock Ownership Plan and discounted employee stock purchases plan. Employees receive a generous matching contribution to 401(k), financial wellness education and consultation to plan for their financial future. In addition to competitive pay, Polaris provides a comprehensive suite of benefits, including health, dental, and vision insurance, wellness programs, paid time off, gym \& personal training reimbursement, life insurance and disability offerings. Through the Polaris Foundation and our Polaris Gives paid volunteer time off, we support employees who actively volunteer their time, efforts, and passions to improve the health and wellbeing of the communities in which they live, play and work. Employees at Polaris drive our success and are rewarded for their commitment.
About Polaris
As the global leader in powersports, Polaris Inc. (NYSE: PII) pioneers product breakthroughs and enriching experiences and services that have invited people to discover the joy of being outdoors since our founding in 1954\. Polaris' high\-quality product line\-up includes the Polaris RANGER®, RZR® and Polaris GENERAL™ side\-by\-side off\-road vehicles; Sportsman® all\-terrain off\-road vehicles; military and commercial off\-road vehicles; snowmobiles; Slingshot® moto\-roadsters; Aixam quadricycles; Goupil electric vehicles; and pontoon and deck boats, including industry\-leading Bennington pontoons. Polaris enhances the riding experience with a robust portfolio of parts, garments, and accessories. Proudly headquartered in Minnesota, Polaris serves more than 100 countries across the globe.
EEO Statement
*Polaris Inc. is an Equal Opportunity Employer and will make all employment\-related decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, national origin, age, disability, marital status, familial status, status with regard to public assistance, membership or activity in a local commission, protected veteran status, or any other status protected by applicable law. Applicants with a disability that are in need of an accommodation to complete the application process, or otherwise need assistance or an accommodation in the recruiting process, should contact Human Resources at 800\-765\-2747 or* *[email protected]**. To read more about employment discrimination protection under U.S. federal law, see:* *Know Your Rights: Workplace Discrimination is Illegal (eeoc.gov)*.
Salary Context
This $79K-$88K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Polaris Industries, 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($83K) sits 61% below the category median. Disclosed range: $79K to $88K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Polaris Industries AI Hiring
Polaris Industries has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Medina, MN, US. Compensation range: $88K - $88K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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
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