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About This Role
SUPPLY CHAIN \& LOGISTICS COORDINATOR
LOCATION
This is an in\-person role that offices from 430 W. Clay Street, Valley Center, KS 67147\.
REPORTS TO
VP Manufacturing \& Engineering
FLSA STATUS
Hourly, nonexempt
POSITION TYPE
Full\-time
SCHEDULE
This role is expected to work to the job, not the time clock, to ensure all departmental functions are properly supported. A minimum of 40 hours per week is required during office hours assigned by the Operations Manager, with additional time as needed based on workload, reporting periods, and business demands.
PAY
Compensation will be determined based on skill, knowledge, experience, and education.
WHY HCS?
Values\-driven company
- Mission First
- People Always
- Raise the Bar
- Take Ownership
Small, American\-owned manufacturing business with a small\-town, family\-oriented environment.
Opportunity to actively participate in multiple disciplines of the Company and make your own impact.
WHAT HORNET PROVIDES
- Competitive compensation
- Retirement plan with Company match
- Paid time off
- Core benefits package including health, dental, and vision
- Supplemental benefits including cancer, hospital, and term life plans
- Simple IRA with Company match up to 3%
POSITION SUMMARY
The Supply Chain \& Logistics Coordinator is responsible for supporting a healthy, efficient supply chain by facilitating communication and coordination across purchasing, inventory, production, shipping, receiving, sales, service, engineering, and distribution functions. This role is responsible for overall purchasing activities, inventory monitoring and management, shipping and receiving coordination, and general logistics support.
There is never a dull moment in the Supply Chain \& Logistics seat at Hornet. As a practical hub of the business, this role identifies and purchases the best product at the best price, serves as a subject matter expert in the ERP system’s inventory and purchasing functions, advocates effectively with vendors and internal customers, and helps connect production, service, sales, and engineering. This role has meaningful influence within the Company and in its interactions with vendors and customers.
The ideal candidate is focused, scrappy, and thrives in a fast\-paced environment with constant activity. This individual must be able to handle pressure with grace and grit, communicate with patience and professionalism, and navigate complex ERP and MRP systems with confidence and accuracy.
ESSENTIAL DUTIES AND RESPONSIBILITIES
Including the following. Other duties may be assigned.
PURCHASING AND SUPPLY CHAIN COORDINATION
- Manage overall purchasing activities to support production, service, and operational needs.
- Identify and source the best product at the best available value while considering quality, lead time, availability, and supplier reliability.
- Create, issue, track, and follow up on purchase orders to support continuity of supply.
- Maintain strong working relationships with vendors and suppliers.
- Coordinate with internal departments to ensure materials and purchased items are aligned with demand, production schedules, and service requirements.
- Monitor supplier performance, delivery timing, shortages, and backorders, and communicate impacts proactively.
INVENTORY MANAGEMENT
- Manage and monitor inventory to support accuracy, availability, and efficiency.
- Maintain accurate digital inventory records and support alignment between physical and system inventory.
- Help oversee inventory movement, adjustments, replenishment, and stock control practices.
- Monitor inventory levels and usage patterns to help prevent stockouts, overstock conditions, and unnecessary carrying costs.
- Utilize ERP or MRP tools to support inventory planning, purchasing decisions, and data accuracy.
SHIPPING, RECEIVING, AND LOGISTICS
- Support and coordinate shipping and receiving activities for inbound and outbound materials, parts, and equipment.
- Help ensure timely, accurate, and cost\-effective movement of goods.
- Coordinate freight arrangements and logistics support while balancing service levels and freight cost management.
- Help ensure orders are received, processed, and shipped accurately and on time.
- Track shipments, deliveries, and logistics activity, and assist in resolving delays, damages, or shipping\-related issues.
- Maintain awareness of transportation, freight, and receiving requirements that impact operations and customer service.
CROSS\-FUNCTIONAL SUPPORT
- Serve as a key communication link between production, service, sales, engineering, accounting, and other departments.
- Maintain a strong working relationship with the Production Supervisor, Service Manager, Engineering, and Accounting to support timely and accurate communication.
- Support both internal and external customers with professionalism, responsiveness, and follow\-through.
- Help identify and support continuous improvement opportunities related to purchasing, inventory, logistics, and communication flow.
- Serve as a knowledgeable internal resource for ERP purchasing and inventory functions.
PERFORMANCE EXPECTATIONS
- Meet the needs of internal and external customers through timely, accurate, and professional support.
- Maintain high ethical standards in all interactions with customers, vendors, and coworkers.
- Support strong on\-time delivery, order accuracy, supplier performance, inventory accuracy, and purchasing visibility.
- Maintain responsiveness, reliability, and accountability in day\-to\-day responsibilities.
- Foster a team environment by supporting coworkers, communicating effectively, and being ready to perform assigned responsibilities when scheduled.
- Look for opportunities to improve methods, processes, and efficiency.
- Uphold and champion Hornet’s core values: Mission First, People Always, Raise the Bar, and Take Ownership.
QUALIFICATIONS AND REQUIREMENTS
- Minimum of 2\-year degree required.
- Bachelor’s degree preferred.
- 3 to 5 years of manufacturing or production\-related experience preferred.
- At least 3 years of purchasing experience preferred.
- Strong computer literacy and proficiency required.
- Proficient in Microsoft Office applications, including Word, Excel, and Outlook.
- Proficiency in one or more ERP or MRP systems required.
- Ability to analyze data, identify trends, and make sound decisions based on operational information.
- Understanding of supply chain, logistics, purchasing, inventory, and transportation concepts.
- Ability to develop, manage, and work within budgets and cost expectations.
- Ability to read and understand production drawings and blueprints.
SUCCESS FACTORS
- Detail\-oriented
- Excellent communication skills
- Effective planning and time management
- Ability to forecast ahead, anticipate issues, and proactively address potential problems
- Strong sense of ownership and accountability
- Ability to adapt to a changing work environment
- Desire to learn and grow
- Professionalism and responsiveness
- Team\-oriented mindset
- Good judgment and problem\-solving ability
PHYSICAL REQUIREMENTS AND WORKING CONDITIONS
The physical demands and working conditions described here are representative of those that must be met by an employee to successfully perform the essential functions of this job, with or without reasonable accommodation.
- Regularly required to sit, stand, walk, speak, hear, and use hands and fingers to operate a computer, phone, and other standard office equipment.
- Frequently required to reach, bend, stoop, and move about the office, production floor, warehouse, shipping and receiving areas, and other operational workspaces.
- Must be able to remain stationary for extended periods while performing computer\-based work, reviewing records, or participating in meetings.
- Must be able to lift, carry, and move ordinary office materials, packages, parts, and similar items up to 25 pounds.
- May be required to inspect, handle, or move inventory and materials in operational areas.
- May be exposed to noise, moving equipment, dust, temperature fluctuations, and other conditions typical of manufacturing, warehouse, and shipping environments.
- Must be able to safely navigate office and production\-related work areas while complying with all safety rules and personal protective equipment requirements.
- Specific vision abilities required by this job include close vision, distance vision, and the ability to adjust focus for computer work, document review, and inventory or materials handling.
DISCLAIMER
This job description is intended to describe the general nature and level of work being performed by the person assigned to this position. It is not intended to be an exhaustive list of all duties, responsibilities, or qualifications. Responsibilities may change at any time based on the needs of the Company. This job description does not constitute an employment contract.
CONTACT
Questions regarding this position or its requirements should be directed to Human Resources.
Pay: $24\.00 \- $29\.00 per hour
Benefits:
- 401(k) matching
- Dental insurance
- Employee assistance program
- Free parking
- Health insurance
- Life insurance
- Paid time off
- Prescription drug insurance
- Retirement plan
- Vision insurance
Work Location: In person
Salary Context
This $49K-$60K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Hornet Cutting Systems, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($55K) sits 67% below the category median. Disclosed range: $49K to $60K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Hornet Cutting Systems AI Hiring
Hornet Cutting Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Valley Center, KS, US. Compensation range: $60K - $60K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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