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About This Role
Software Developer (Automation Tools \& AI Enablement)
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Our automation engineers are very good at what they do, and they still lose hours every week rebuilding things they have already built. Tag databases checked by hand, line by line. Alarm configs recreated from scratch on a project that looks a lot like the last one. The same validation done five different ways by five different people, all of them defensible, none of them shared.
You are here to fix that. Not by telling engineers to work differently, but by building the tools that make the better way the easier way.
This is a fully remote position.
Who We Are
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At Vertech, not only do we develop world class industrial automation solutions for some of the top US companies, we also strive to be the team of choice for the best and brightest talent. I mean, we call ourselves control freaks for a reason. The passion (and intelligence) our employees have is truly mind\-blowing. In turn, we work hard to take care of our own with excellent benefits and pay, personal development, and transparent communication. We also know that you can be great at what you do and still have fun. If that sounds like a good fit, help us bring the next generation of automation solutions to nearly every industry, including manufacturing, food and beverage, renewable energy, water and wastewater, baggage handling and more.
Top Reasons to Work with Us
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Weekly communication from management in our internally famous Friday morning meeting
No more than a 4 week waiting period on all benefits including the 401k
A really rad Christmas Party each year
A manager that is cool…most of the time, because no one is perfect
A path for growth, a great culture, PTO, AND MORE!
What You Will Be Doing
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You build internal software for the people who build automation systems. Your users are the engineers down the hall, which means you get direct feedback, you see your tools in use, and you find out fast when something does not work. That is the best part of the job and occasionally the worst part.
Design and build internal tools that take repetitive engineering work off your teammates' plates
Write utilities, scripts, and services in Python, SQL, JavaScript, and whatever else fits the problem
Build automated testing and validation for automation project artifacts, so problems get caught before deployment instead of during commissioning
Champion source control, branching strategy, and CI/CD using Git and Azure DevOps, in a world where that is not yet the default
Find the places where every project starts from zero, and build the reusable thing that ends it
Evaluate and introduce AI\-assisted workflows for documentation, code generation, validation, and knowledge retrieval, and be honest about where AI does not help
Sit with engineers, watch how they actually work, and turn what you learn into tooling people choose to use
Write the docs and training that make your tools stick after the launch conversation is over
Keep a real backlog and prioritize the work with the biggest operational payoff
Example Projects
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Tools that generate or validate PLC tag structures, alarm definitions, and project configuration artifacts
Utilities that parse and convert automation platform import/export files, including PLC tag databases and SCADA configuration files
Automated quality checks that catch common project issues before they reach the field
CI/CD pipelines and automated testing for automation project assets
Internal applications that compare project versions, analyze system configurations, or check standards compliance
AI\-assisted workflows for documentation generation, code review, standards validation, and engineering knowledge retrieval
Integrations that connect engineering tools to version control, documentation platforms, and project management systems
What You Need for this Position
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3 to 5 years building software, with real depth in Python and comfort in SQL or JavaScript
Experience in or alongside industrial automation. SCADA, PLCs, controls, manufacturing systems, systems integration, or OEM work all count. You should be able to talk about tag databases, alarm configs, and vendor file formats without needing them explained.
A track record of building tools other people actually used. Not just scripts that solved your own problem, but something you handed to a team and got adopted.
Working knowledge of version control, CI/CD, and automated testing in practice, not just in theory
The instinct to standardize. You notice when something is being rebuilt for the third time and you do something about it.
Ability to explain technical work clearly to engineers who are not software developers, and to take their feedback without getting defensive about your design
No degree required. We care about what you have built.
Nice to Have
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Hands\-on time with Ignition, or with Rockwell, Siemens, Schneider Electric, or GE ecosystems
Experience using AI\-assisted development tools in real work, with a clear\-eyed view of where they help and where they do not
APIs, data processing, and automation scripting
Agile or iterative development environments
Any exposure to industrial manufacturing processes
Show Us What You've Built
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We love candidates who build things. Send links to tools, scripts, GitHub repos, technical writeups, or anything else you have made. A rough internal utility that saved your team ten hours a week tells us more than a polished side project.
Please do not share proprietary or confidential code from a previous employer. We want to see how you think, not your last company's IP.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At VERTECH, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
VERTECH AI Hiring
VERTECH 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 $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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