Interested in this AI/ML Engineer role at PartsBadger LLC?
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Why PartsBadger?
PartsBadger changes the way people and companies source their custom manufactured parts. We believe in a do\-whatever\-it\-takes attitude to get the job done. We accept the feedback of others and take ownership of our actions. If you are open\-minded and ready to be a part of something bigger than yourself with a winning team, continue reading…
What is the Position?
PartsBadger is seeking a Director of AI Transformation to lead the strategy and execution of artificial intelligence adoption across our entire organization. This is not a theoretical role — you will be in the trenches with every department, understanding how they work, identifying where AI creates real value, and driving implementation from concept to live deployment.
You will serve as the bridge between our business operations and our software development team, ensuring that the tools we build and adopt actually solve the problems our people face every day. This position reports to the VP of Software.
If you are energized by change, thrive at the intersection of technology and operations, and know how to bring skeptical people along on a journey — this role was built for you.
Work Environment
PartsBadger is a relaxed, casual work environment located in Cedarburg, WI. We are ITAR registered and hold AS9100 and ISO9001:2015 certifications. Our 15,000 sq. ft. facility blends office and production space. We offer fully stocked snack/beverage rooms, free daily lunches, and a 5\-hole disc golf course. We continue to develop our space for idea generation and collaborative work.
Duties \& Responsibilities
Day to Day
- Embed yourself across all departments — Shop/Production, Sales \& Marketing, Engineering/CAM, Finance, and Operations — to deeply understand their workflows, pain points, and opportunities
- Stay current on emerging AI technologies and evaluate their relevance to our business
- Champion a culture of innovation, experimentation, and continuous improvement
- Lead evaluation, selection, and deployment of AI tools and platforms across the business
- Act as the product lead on behalf of departments to shepherd deployments.
Management
- Translate departmental needs into actionable technical requirements
- Define requirements, set priorities, and ensure solutions are built or integrated to spec
- Oversee testing, rollout, and iteration of AI\-powered tools and workflows
- Develop documentation, training, and support materials for each deployment
- Manage change across the organization, driving buy\-in at every level
- Report AI program status, wins, and risks directly to VP of IT and executive leadership
Strategic
- Help develop and own the company\-wide AI adoption roadmap, aligned with PartsBadger's growth goals
Required Qualifications
- 5\+ years of experience in deploying technology within departments.
- Demonstrated experience deploying AI or automation solutions in a business environment
- Strong understanding of AI/ML concepts, LLMs, automation tools, and data workflows
- Ability to translate complex technical concepts into plain language for non\-technical stakeholders
- Proven track record of cross\-functional collaboration and influencing without direct authority
- Strong understanding of business functions including, HR, finance, sales, supply chain, production, shipping, etc.
- Excellent verbal and written communication skills
- Strong project management skills — able to manage multiple initiatives simultaneously
- Adaptable, fast\-moving, and comfortable with ambiguity
- Independent problem solver with a bias toward action
Preferred Qualifications
- Experience in a manufacturing or industrial operations environment
- Familiarity with ERP, CRM, CAM, or MES systems
- Experience working directly with software development teams
- Background in data science, analytics, or business intelligence
- Bachelor's degree or higher in a relevant field
Opportunity for Advancement
This position is structured to allow for advancement in a variety of areas with flexibility to try new things and challenge your mental boundaries. PartsBadger understands the value of our team and we work hard to nurture creativity and engagement.
Salary Context
This $110K-$155K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At PartsBadger LLC, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($132K) sits 27% below the category median. Disclosed range: $110K to $155K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
PartsBadger LLC AI Hiring
PartsBadger LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cedarburg, WI, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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