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About This Role
Automation and AI Engineer
Join a diverse team at the center of America’s aerospace supply chain, trusted by Boeing and its sub\-tiers with highly engineered parts that have zero room for error. We’re known for tackling complex, high\-precision surface finishing for aerospace, defense, and space, and we treat quality like a craft.
You’ll work alongside hands\-on problem\-solvers who take pride in quality, collaboration, and continuous improvement. Our leaders are engaged and approachable, and they are committed to mentoring early\-career professionals while providing meaningful ownership and opportunities to build practical AI solutions that improve how work gets done.
What we look for in this role: analytical thinking, technical curiosity, sound judgment, strong communication, collaboration, attention to detail, and a drive to solve practical business problems through technology.
Position Overview
Title: Automation and AI Engineer
Reports to:VP of Finance
Location: Kent, WA
Type:Exempt (Salaried)
Work Schedule: 1st Shift; Full Time
Salary: $73,000 \- $99,000; placement within this range will be based on education, internships, project experience, technical skills, and overall qualifications.
Full benefits package includes: Medical, Dental, Vision, 401(k) matching, Performance Share incentive compensation (Profit Sharing Plan), 3\-weeks paid time off to start, paid community service day, tuition reimbursement, parental leave, short term disability, long term disability, life insurance, long\-term care plan options, 10 paid holidays, and onsite health services that includes access to a massage therapist.
Job Summary
The Automation and AI Engineer supports Hytek Finishes’ continuous improvement efforts by identifying, testing, and implementing practical automation and AI\-enabled solutions across the business. Working with Quality, Operations, IT, Finance, and frontline teams, this role helps map current processes, improve data accuracy and visibility, reduce manual work, and document repeatable workflows. The position is well suited for a recent graduate or early\-career professional with strong analytical skills, technical curiosity, and an interest in applying data, automation, and AI tools to real business problems. This position reports to the VP of Finance.
Key Responsibilities
- Identify opportunities to improve business processes through automation, data analysis, and practical AI\-enabled tools
- Assist with the design, development, testing, and implementation of automation workflows, scripts, reports, and AI\-supported solutions
- Use programming, data analysis, and machine learning concepts to support projects that improve quality, efficiency, and decision\-making
- Evaluate software, reporting, and internally developed solutions that may reduce manual work or improve process consistency
- Build, test, monitor, and refine models or automation tools under appropriate guidance as business needs evolve
- Support project planning, including phases, testing steps, documentation, training materials, and implementation activities
- Manage project tracking (timelines, action items, meeting notes) and provide weekly status updates on progress, risks, and next steps
- Support user testing by developing test cases, executing test scripts, documenting issues, and coordinating fixes with internal teams or vendors
- Create clear documentation and training materials (quick reference guides, work instructions) and help train end users on new processes and tools
- Coordinate with cross\-functional partners to ensure changes are practical, support accurate reporting, and meet business compliance requirements
- Develop visualizations, reports, and technical documentation that communicate findings clearly to technical and non\-technical audiences
- Help deploy, maintain, and support automation and AI solutions so they remain accurate, reliable, and integrated with existing workflows
- Troubleshoot data, software, model, or system issues and escalate more complex technical matters as needed
- Maintain organized project files, version control for templates, and accurate documentation to support repeatable processes and audits
- Stay current on automation, AI, and machine learning tools and best practices, and perform other duties as assigned
Education \& Experience Minimum Qualifications:
- Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field
- 0\-2 years of relevant experience; internships, academic projects, research experience, or equivalent hands\-on work are acceptable
- Experience with programming languages such as Python, R, Java, or similar
- Exposure to machine learning frameworks
- Familiarity with data structures, algorithms, and statistical analysis
Knowledge, Skills, and Abilities:
- Foundation in machine learning concepts, statistical modeling, programming fundamentals, and software development practices.
- Ability to manipulate and analyze data, debug technical issues, and document code, models, and processes effectively
- Strong Excel skills (pivot tables, lookups, basic data cleaning); familiarity with Power Query/Power Pivot, VBA, SQL, or Python is a plus
- Comfortable learning and working in business systems (ERP/MRP, receiving, inventory, planning); prior exposure to ERP systems is a plus
- Strong analytical thinking, problem\-solving ability, and attention to detail with sound mathematical and statistical aptitude
- Strong written and verbal communication skills with the ability to explain technical concepts to a range of audiences
- Proven ability to work collaboratively in a team environment and manage routine assignments under direct supervision
- Ability to handle sensitive business information appropriately and follow data and access controls
- Team player with a proactive mindset, curiosity, and willingness to work on the production floor and in office settings as needed
- Ability to read, write, and communicate in English and maintain a regular and dependable attendance record
- Must be able to pass a criminal background check and pre\-employment drug screening
- Must be authorized to work in the U.S. An export license will be required to work in all areas of the facility
Essential Mental/Physical Demands:
Ability to sit in an office for long periods but also comfortable with working in a manufacturing environment as well. Extensive use of hands in writing, computer and using mechanical equipment. Ability to lift and carry up to 25 lbs. on an occasional basis. Occasionally exposed to moving mechanical parts, fumes or airborne particles, and toxic or caustic chemicals. The noise level in the work environment is usually moderate. May need to wear PPE such as safety glasses or goggles in some areas.
Application Process
Please apply at www.hytekfinishes.com/careers with an up\-to\-date resume and your current contact information. If you are a qualified candidate, we will set you up with a 30\-minute phone call with a recruiter to learn more about your experience and to tell you more about the job, compensation, and benefits. The recruiter will reach out should we move forward with the next steps.
*Hytek Finishes is an equal opportunity employer. It is Hytek’s policy to make all employment decisions without regard to an individual’s sex, gender (including pregnancy, childbirth, breast feeding, and related conditions), sexual orientation, gender identity, gender expression, race (including traits historically associated with race, including hair texture and protective hairstyles such as braids, locks and twists), creed, religion (including religious dress and grooming), color, national origin, ancestry (including association, affiliation, or participation with persons or activities related to national origin, English\-proficiency or accent or immigration status), physical or mental disability, medical condition, genetic information, marital or domestic partner status, age, veteran or military status, and any other categories protected by applicable state, local, or federal law. M/F/Disability/Protected Vet*
Salary Context
This $73K-$99K 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 Hytek Finishes, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($86K) sits 53% below the category median. Disclosed range: $73K to $99K.
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.
Hytek Finishes AI Hiring
Hytek Finishes has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Kent, WA, US. Compensation range: $99K - $99K.
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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