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About This Role
Company Background
Taymer International Inc. Is a rapidly growing innovator of camera inspection systems, printing solutions, and length measurement devices for the wire and cable and medical manufacturing industries. Taymer develops advanced quality inspection and process control systems used in continuous manufacturing environments around the world. Please see our website for more complete product information: www.taymer.com
Position Summary
As Taymer continues to expand, we are looking for a highly motivated Machine Field Engineer to join our growing team in Texas. This role is ideal for an engineer with a strong background in mechatronics, automation, and machine vision, who enjoys hands\-on technical work, problem\-solving, and customer interaction.
The successful candidate will travel to customer facilities to install, commission, troubleshoot, and support advanced inspection and automation systems. This position combines mechanical, electrical, software, and control systems work in fast\-paced manufacturing environments.
We are looking for someone who is self\-driven, organized, technically curious, and comfortable working independently while collaborating closely with customers and internal engineering teams. Experience with robotics, computer vision, PLCs, embedded systems, sensors, is highly valued.
Location
Texas, USA
Skills
- Strong communication and customer interaction skills
- Self\-motivated with the ability to work independently and remotely
- Excellent troubleshooting and analytical problem\-solving abilities
- Understanding of electrical circuits, sensors, motors, and control systems
- Knowledge of PLCs, HMIs, industrial automation, and machine controls
- Familiarity with robotics, machine vision, computer vision, or autonomous systems
- Experience with Python, C\+\+, and similar programming languages
- Comfortable working with mechanical assemblies and electro\-mechanical equipment
- Ability to document technical findings and maintain organized service records
- Proficient with Microsoft Office Suite
- Valid driver’s license and willingness to travel frequently
Education Requirements
- Bachelor’s degree in Mechatronics Engineering, Electrical Engineering, Mechanical Engineering, Robotics, Physics, or related technical field
Preferred Experience
- Experience with robotics, computer vision, or sensor integration
- Familiarity with industrial controls, Ladder Logic, TIA Portal, SCADA, or HMIs
- Experience with machine vision systems, cameras, lighting, or optics
- Experience with CAD software such as SolidWorks, Fusion 360, or AutoCAD
- Demonstrated experience working on multidisciplinary engineering projects
- Internship, capstone, research, robotics competition, or project\-based engineering experience considered valuable
- Experience in manufacturing, automation, medical devices, or wire \& cable industries is an asset
Duties and Responsibilities
- Install, commission, and support Taymer inspection and measurement systems at customer facilities across North America
- Troubleshoot electro\-mechanical, software, and control system issues in industrial manufacturing environments
- Configure and optimize machine vision systems, sensors, lighting, and inspection parameters
- Provide customer training, technical support, preventative maintenance, and remote troubleshooting assistance
- Collaborate with customers to integrate Taymer systems into automated production lines
- Assist Engineering teams with testing, assembly, system validation, and new product development initiatives
- Support integration work involving PLCs, HMIs, industrial communication protocols, and automation systems
- Document service activities, technical findings, and customer requirements clearly and professionally
- Represent Taymer professionally at customer sites, demonstrations, and industry trade shows
- Travel frequently throughout the United States, with opportunities for international travel
What We Can Offer You
- Flexible work hours
- Competitive salary based on experience and technical abilities
- Hands\-on experience with advanced machine vision and automation technologies
- Exposure to cutting\-edge applications in medical manufacturing and industrial automation
- A collaborative and innovative engineering culture focused on continuous learning and development
- Medical benefits
- Opportunities for technical growth, international travel, and training
- Annual trips to Canada for technical training, business planning, team events, BBQs, cottage days, and more
Benefits:
- 401(k)
- Dental insurance
- Flexible schedule
- Health insurance
- Life insurance
- Paid time off
- Retirement plan
- Tuition reimbursement
- Vision insurance
Education:
- Bachelor's (Required)
Work Location: Remote
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 Taymer America, 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.
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.
Taymer America AI Hiring
Taymer America has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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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