Interested in this AI/ML Engineer role at Western Welding Academy?
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About This Role
AI & Automation Engineer
Western Welding Academy | Gillette, WY | Full-Time | In Person or Remote
Pay: $100,000 – $130,000 per year, based on experience Job Overview
Western Welding Academy is on a mission to build a better generation of blue-collar craftsmen. We train skilled welders through work ethic, accountability, and integrity, and we focus on student outcomes above everything else. We are a growing school with more good ideas than time to build them. We need a strong engineer to design, build, and own the AI systems and tools that run our business. You'll write real code, connect our systems, and ship apps and AI agents our team uses every day. You'll also take in project requests from across the company, decide what matters most, and see each project through to launch.
Important Eligibility Requirement
Applicants must already be legally authorized to work in the United States. Western Welding Academy does not sponsor work visas or offer employment-based immigration support.
Why Join Western Welding Academy?
You'll see your work make a real difference, fast. There's no red tape and no waiting months for approval. You'll work directly with leadership, own the technical direction for AI across the company, and every hour you save our team goes back into helping students succeed.
What's In It for You
- Free medical option. Our Cigna HDHP is fully company-paid for you and your family. That's $0 out of your paycheck.
- Money toward your health costs. Choose the HDHP and we add $1,000 a year to your Health Savings Account.
- Company-paid life insurance. $75,000 in coverage at no cost to you.
- 401(k) with a match. We match up to 3.5% of your pay.
- Dental and vision through Principal at a low monthly cost.
- Generous PTO from your very first day.
- Nine paid holidays, including Good Friday, Black Friday, and Christmas Eve.
- A mission that matters. Your work helps put people into life-changing careers.
What You'll Do
Engineer AI solutions
- Design, build, and deploy internal apps and tools using modern AI software and coding tools (such as Claude, Replit, or similar).
- Build AI agents and assistants that plug into our real business processes in admissions, marketing, housing, HR, finance, and operations.
- Integrate large language models (LLMs) into our systems using APIs, including prompt design, testing, and tuning for accuracy.
- Write clean, maintainable code in Python, JavaScript, or TypeScript.
- Set up and manage databases and data pipelines that feed our tools and reporting.
Connect our systems
- Build and maintain integrations between our CRM (HubSpot), Google Workspace, Slack, finance tools, and other platforms using APIs and webhooks.
- Use automation platforms (such as Make, Zapier, or n8n) where they are the fastest fit, and write custom code where they aren't.
- Keep our systems secure, protect student and employee data, and follow best practices for access and credentials.
Manage the project pipeline
- Own the intake process for AI and automation requests across all departments.
- Score and prioritize projects based on time saved, cost, and impact on the business.
- Set timelines, track progress, and keep leadership updated on status.
- Manage outside vendors or freelancers when a project needs extra help.
Make it stick
- Monitor, fix, and improve everything you ship so it keeps running.
- Train staff on new tools and make sure they actually get used.
- Document your systems so others can understand and support them.
- Track results and report the time saved and business impact.
- Stay current on AI tools and bring new ideas to the table.
What We're Looking For
- 4+ years of software engineering, automation, or AI development experience.
- Strong coding skills in Python, JavaScript, or TypeScript.
- Hands-on experience building with LLM APIs (Anthropic, OpenAI, or similar), including AI agents or tool integrations.
- Solid experience with REST APIs, webhooks, and connecting third-party platforms.
- Experience with databases (SQL or similar) and cloud hosting or deployment.
- Proven experience managing multiple projects at once, from intake to launch.
- A portfolio, GitHub, or examples of systems you've built and shipped. Please include this with your application.
- The ability to explain technical work in plain language to non-tech people.
- Strong follow-through. You close loops and don't let things fall through the cracks.
- Nice to have: HubSpot development, experience with Make/Zapier/n8n, project management tools (Asana, Monday, ClickUp), or experience with EOS.
Work Schedule
- Full-time, in person in Gillette, WY, or remote.
- Some flexibility for evenings or weekends when projects require it.
- Remote candidates must be available during our business hours (Mountain Time) and able to travel to Gillette on occasion.
- In-person candidates must be able to travel between Western Welding Academy properties in Gillette.
Join Our Mission
If you love building systems that work, leading projects, and seeing the results right away, we'd love to talk to you. Apply today and help us build the future of welding. Before You Start
All offers are contingent upon successful completion of a pre-employment drug screening and background check.
Benefits
401(k), 401(k) matching, Dental insurance, Health insurance, Health savings account, Life insurance, Paid time off, Parental leave, Referral program, Retirement plan, Vision insurance
Work Location: In person or remote
Salary Context
This $100K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Western Welding Academy, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($115K) sits 46% below the category median. Disclosed range: $100K to $130K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Western Welding Academy AI Hiring
Western Welding Academy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Gillette, WY, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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