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About This Role
Description: WHO WE ARE
Johnson Health Tech is one of the world’s largest and fastest-growing fitness equipment manufacturers, delivering innovative solutions that help people live healthier lives. Through our award-winning portfolio of commercial and residential brands, we serve customers in more than 60 countries worldwide.
MISSION STATEMENT
To delight and excite our customers through truly innovative products, unsurpassed quality, and best-in-class customer experience provided by a world-class organization.
POSITION OVERVIEW
We are building an internal AI adoption program to help teams across the organization use AI tools more effectively in their daily work. The AI Business Analyst is the operational engine of that program: part BA, part administrator, part trainer, and part internal consultant. In this highly cross-functional role, you will work closely with Finance, Customer Service, Marketing, Operations, and Sales to identify opportunities, support workflows, maintain governance, and measure impact.
RESPONSIBILITIES
Program Administration
- Manage day-to-day operations of the AI tools adoption program, including access provisioning, tier management, and onboarding coordination
- Maintain and update the AI Prompt Library — reviewing submissions, testing new prompts, tagging, and publishing approved content
- Track training completion across teams and coordinate with the LMS administrator to ensure records are accurate
- Schedule and facilitate AI Office Hours sessions; summarize questions and common issues for IT leadership
- Maintain program documentation, including policy updates, FAQs, and workflow guides
Training & Enablement
- Assist in the delivery of AI Tools Intro training and use-case workshops for specific teams
- Develop supplemental training materials — job aids, quick-start guides, use-case walkthroughs — as new workflows are identified
- Build and maintain a library of worked examples: real prompts + outputs (sanitized) that illustrate effective AI use across business functions
- Serve as a first-line resource for employees with questions about how to use AI tools effectively and safely
Workflow Development & Testing
- Partner with team leads to identify repetitive, high-volume tasks that are strong candidates for AI assistance
- Prototype and test AI-assisted workflows (using Claude and Claude Code), document results, and create guides for broader rollout
- Support web development and marketing teams in using Claude Code for landing pages, A/B test variants, and site work — acting as a liaison between technical and non-technical stakeholders
- Evaluate and test new AI tools or features as they become available; provide structured recommendations to IT leadership
Governance & Compliance Support
- Monitor adherence to the AI Tools Governance Policy; flag potential violations for IT Security review
- Assist with incident documentation and initial triage when data handling issues are reported
- Support the quarterly access tier review process
- Contribute to policy updates as the program evolves and new use cases emerge
Requirements: Education:
- Bachelor’s degree in Information Technology, Business Administration, or a related field.
Experience:
- 3+ years of experience in a business analyst, program coordinator, IT operations, or related role
- Demonstrated experience using AI tools (ChatGPT, Claude, Copilot, or similar) in a professional setting
- Strong written communication skills — able to write clear, audience-appropriate documentation and training materials
- Comfortable working across departments and translating between technical and non-technical audiences
- Organized and self-directed; able to manage multiple workstreams without close supervision
- Proficiency with standard productivity tools (Microsoft 365 or Google Workspace, project tracking tools, LMS platforms)
Travel:
- A valid driver’s license is required for local travel
- This position will be required to travel up to 10% of the time.
PHYSICAL REQUIREMENTS & WORK ENVIRONMENT
Physical Requirements
While performing the duties of this job, the employee is regularly required to sit for extended periods, use hands and fingers to operate a computer keyboard, mouse, phone, and other standard office equipment. Will be required to communicate both verbally and in writing, view a computer monitor, and perform tasks requiring close vision with the ability to adjust focus. The employee is occasionally required to stand, walk, reach, bend, stoop, kneel, or crouch to access files, supplies, or other office equipment. The employee must occasionally lift, carry, push, or pull materials weighing up to 20 pounds. Repetitive hand, wrist, and finger motion is required for keyboarding and other clerical tasks.
Standard Office Environment
This position will require working on-site. The employee must be able to maintain concentration in a variety of work environments and sustain attention to detail while managing multiple priorities. The position requires the ability to reason, analyze information, and make sound decisions promptly. The employee must also be capable of working both independently and collaboratively and of interacting professionally with colleagues, leadership, and external partners.
BENEFITS & PERKS
- Medical, dental, and vision insurance
- Company-paid life insurance and AD&D coverage
- 401(k) retirement savings plan
- Paid time off (PTO)
- FSA and HSA options
- Employee product discounts, wellness resources, and employee assistance programs
Equal Opportunity Employer, including Protected Veterans and Individuals with Disabilities.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Johnson Health Tech, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Johnson Health Tech AI Hiring
Johnson Health Tech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cottage Grove, WI, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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