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We are Trusted Partners | Supporting Flight. Our more than 50 years of success is the result of our employees who have dedicated themselves to providing unmatched quality, service and safety in aircraft maintenance, repair and overhaul. Being a part of ATS offers an opportunity to build meaningful partnerships, innovate new ideas, do the right thing, and make our company a great place to work!
Your Work Matters – And So Do You
At ATS, we reward your contributions with a well-rounded compensation package designed to support you today and help you plan for tomorrow:
- Hiring base pay range: $36 - $44 hourly, plus annual bonus eligibility
- Comprehensive health coverage – medical, dental, vision, and more
- 401(k) with company match and access to personal financial advisors
- Generous time off – PTO, holidays, and paid parental leave
- Tuition assistance and loan repayment for AMT programs
- On-the-job training and leadership development through ATS Academy
- Employee discounts on travel, tech, wellness, and more
- Confidential 24/7 support through our Employee Assistance Program
Join a team that values your growth, your well-being, and your future.
SCHEDULE: The schedule for this position is Monday through Friday, 7:00am – 3:30pm
TRAVEL: Required travel to Kansas City, Missouri, for implementation period (estimated 8 weeks total, split between 2-3 separate visits) and monthly recurring visits after completion of implementation.
SUMMARY: The AI Implementation & Adoption Specialist supports the successful adoption, day-to-day use, and operational sustainment of ATS artificial intelligence solutions. The position serves as a hands-on resource for end users by providing floor-level coaching, training, onboarding, documentation, diagnosing technical issues, and adoption support across shifts, departments, and ATS locations. The role works closely with the AI Project Manager, business stakeholders, IT, and external AI partners to ensure AI solutions are effectively implemented, supported, and continuously improved
ESSENTIAL DUTIES AND RESPONSIBILITIES:
- Provides hands-on, floor-level support for ATS AI solutions, including Mechanic Copilot and future AI tools deployed across the organization.
- Coaches end users on effective use of AI tools and reinforces approved workflows, use cases, and operating practices.
- Supports AI adoption across multiple shifts by answering user questions, resolving routine issues, and escalating technical or product issues to the appropriate ATS or vendor resource.
- Conducts new-user onboarding, refresher training, small-group training sessions, and other AI enablement activities.
- Supports the AI Ambassador program through coaching, resources, follow-up, and coordination with designated business-area ambassadors.
- Creates and maintains user-facing documentation, quick-reference guides, FAQs, training materials, tips and tricks, and other adoption resources.
- Monitors user adoption and engagement trends using available administrative and analytics tools and identifies areas requiring additional training, communication, or support.
- Collects end-user feedback, recurring questions, pain points, and enhancement ideas and communicates findings to the AI Project Manager for evaluation and prioritization.
- Supports rollout and implementation activities at new ATS lines, departments, customers, and locations, including extended onsite support as needed.
- Assists with user administration activities such as onboarding users, validating access, and coordinating account additions or removals.
- Maintains functional knowledge of AI solution architecture, knowledge sources, integrations, access controls, system behavior, and product limitations sufficient to support implementation and troubleshooting.
- Diagnoses and triages AI application issues by identifying user or prompting issues, access or configuration problems, source-data or content issues, unexpected application behavior, and potential product defects.
- Reproduces and documents technical or product issues and works with ATS IT and external AI partners to communicate findings, support escalation, and validate resolution.
- Supports technical readiness for new AI implementations by validating user access, configurations, workflows, and user-facing functionality before and during rollout.
- Works cross-functionally with Operations, Supply Chain, IT, Training, Quality, and other business areas to promote effective use of approved AI solutions.
- Maintains working knowledge of approved AI capabilities, product updates, emerging use cases, and ATS governance requirements
EDUCATION AND EXPERIENCE:
- Bachelor's degree in information technology, business, training and development, operations management, or a related field required.
- Minimum of two (2) years of experience in information technology, information systems, enterprise software, AI-enabled applications, or technical support required; or an equivalent combination of education, experience and training.
- Demonstrated experience troubleshooting and supporting enterprise software or AI-enabled applications in a user-facing environment required
PHYSICAL EFFORT AND DEXTERITY: While performing the duties of this job, the employee will be required to sit, stand, and walk; use their hands and arms; and talk or hear; and focus on objects close up. The employee may be required to lift and/or move up to 10 pounds and occasionally lift and/or move up to 25 pounds.
Compensation
The full base pay range for this position is $36 - $53 hourly, not including bonuses or benefits. Your pay will be based on your experience, skills, and education, as well as internal equity to ensure fairness across the team. Candidates closer to the lower end of the range are typically building their expertise, while those at the higher end bring significant experience and specialized knowledge. This position is also eligible for an annual performance bonus.
EEO EMPLOYER STATEMENT: ATS is committed to providing equal employment opportunities to all employees and applicants, and prohibits discrimination, harassment, and retaliation of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic or status protected by federal, state, or local laws. ATS is also committed to protecting its applicants for employment and independent contractors from unlawful discrimination, harassment, and retaliation. Provisions in applicable laws providing for bona fide occupational qualifications or business necessity will be adhered to where appropriate.
As part of our Equal Employment Opportunity policy, ATS complies with all applicable federal, state, and local employment laws. Where required by law, ATS maintains affirmative action programs for protected veterans and qualified individuals with disabilities. Employment decisions are based on merit, qualifications, and business needs, consistent with applicable legal requirements.
Salary Context
This $74K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Aviation Technical Services, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($92K) sits 56% below the category median. Disclosed range: $74K to $110K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Aviation Technical Services AI Hiring
Aviation Technical Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Everett, WA, US. Compensation range: $110K - $110K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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