Interested in this AI/ML Engineer role at Tennant Company?
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About This Role
With more than $1B in revenue, Tennant Company is a globally recognized leader in the cleaning equipment industry. For the past 150 years, we have been passionate about developing and manufacturing innovative and sustainable solutions for our customers. At Tennant Company, we are committed to stewardship and creating a cleaner, safer and healthier world. With manufacturing, operations and sales, service, and support functions across the globe, your journey at Tennant can take you places you never expected.
Tennant Company seeks a full\-time Machine Learning Engineer based in Golden Valley, MN. Responsible for utilizing experience in emerging technologies such as Cloud computing, Big Data, Deep Machine Learning, and AI. Participates and leads phases of the data science process, from data exploration and processing, feature selection and engineering, model training and testing, and information synthesis. Req: Master’s degree or equivalent in Computer Science, Data Science, Robotics, Statistics, or a related field and one (1\) year of related software development experience. Must also have demonstrated experience with each of the following: 1\) Utilizing experience in emerging technologies such as Cloud computing, Big Data, Deep Machine Learning, and AI, and partnering with internal stakeholders to understand business needs, capture requirements, identify and prepare key data sources, develop and deploy data driven ML solutions targeting specific business challenges. 2\) Developing and maintaining NLP (Natural Language Processing) multi\-class classification and predictive maintenance models. 3\) Implementing knowledge of ML techniques/algorithms, including linear models, neural networks, decision trees, Bayesian techniques, clustering, and anomaly detection. 4\) Working with cloud\-based platforms (Azure or AWS), and Machine learning/Artificial Intelligence services such as Azure ML or AWS SageMaker. 5\) Providing deep insights from business data using Power BI, Tableau or similar business intelligence tools. 6\) Utilizing the following tools and technologies: Azure AI Foundry, Apache Hadoop, Spark, Kafka, HBase, Hive, Storm and managed services such as Azure HDInsight, Databricks and Azure Datafactory. All experience may have been gained concurrently. Experience may have been acquired before, during, or after completion of the Master‘s degree program. This is a hybrid role with required 3 days onsite. Salary: $119,642 \- $122,600/year. Please apply online at https://jobs.tennantco.com/. Benefits \= A comprehensive benefits package including health insurance, 401(k), disability, life insurance, paid time off, and voluntary benefits!
Total Compensation \= Pay range \+ Benefits
Posted salary ranges are made in good faith. Tennant Sales and Service Co. reserves the right to adjust ranges depending on the experience/qualifications of the selected candidate as well as internal and external equity. The salary range reflects both entry into the role and future growth. Total Compensation \= Base Salary \+ Benefits
Benefits \= A comprehensive benefits package including multiple medical plan options, 401(k) with a 100% match up to 6%, paid vacation time, up to 8 paid company holidays and 3 personal holidays per year, dental and vision coverage, wellness rewards, robust family support programs, company‑paid disability and life insurance, and a full Employee Assistance Program.
Defining Our Employee Value Proposition
At Tennant Company, we…
Discover fulfilling work with opportunities for growth and meaningful recognition.
Drive the quality and innovation our customers rely on in our products and services.
Connect with people who care about each other, our brands and our communities.
These are the principles that define how our employees experience Tennant Company, make Tennant a great place to grow your career, and are at the core of our legacy and future. Together, they serve as guideposts for working with our customers, our partners and one another.
Begin your journey with us. Let's reinvent how the world cleans.
Equal Opportunity Employer
Tennant Company is an equal opportunity employer. Employment decisions are made on the basis of individual skill, ability, reliability, productivity, and other factors important to performance. We do not discriminate on the basis of race, color, creed, religion, sex, national origin, physical or mental disability, age, veteran status, pregnancy, sexual orientation, genetic information, gender identity, or any other basis protected by state or federal law or local ordinance.
Salary Context
This $119K-$122K range is in the lower quartile 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 Tennant Company, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($121K) sits 45% below the category median. Disclosed range: $119K to $122K.
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.
Tennant Company AI Hiring
Tennant Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Golden Valley, MN, US. Compensation range: $122K - $122K.
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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