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About This Role
The short version
We are a managed IT and compliance provider for dental practices. Over the last two years our owner and our Director of IT built an unusual amount of automation — roughly 35 production workflows, two AI voice agents that answer client calls after hours, internal apps our staff use daily, and a client portal that is about to launch.
Neither of them is a software engineer. They built it with a troubleshooting mindset, AI coding assistants, stubbornness, and a deep understanding of how our business works. We are looking for someone with an MSP background and the same instincts, and the two of them are going to teach you to run it.
If you are a service desk technician who has hit the ceiling of what tickets can teach you, and you have been quietly automating bits of your own job because the manual version annoyed you, this is written for you.
What we are honestly offering
A trade, stated plainly. We are paying what an experienced technician earns rather than what a fully\-formed automation engineer costs, because you will not be one on day one. In exchange you get direct training from the two people who built all of this, and six months of protected ramp to become one.
- You will be trained, not thrown in. The first three months you are learning and documenting, not carrying a pager. Scope arrives in stages and we have written down what you own when.
- You will finish your first year with skills that were not on your resume when you started — API work, workflow automation, enough TypeScript to be useful, and the judgment to run a platform. Those are portable whether you stay or not, and we would rather be honest that they are yours.
- Formal reviews at month three and month six, against written deliverables rather than impressions. You will always know where you stand.
- You will be the only person doing this work, which means real ownership rather than a slice of someone else's project.
- The people training you are the owner and the Director of IT. Not a manager relaying instructions — the people who actually wrote it.
The honest counterpart: the systems you inherit are load\-bearing and largely undocumented, and a good part of your first few months is sorting out that mess. Some people find that satisfying and some find it grim. Worth knowing which you are.
What the work actually is
Once you are up to speed, roughly:
- Keeping our automation running, and being the person who finds out first when something breaks. Right now we find out because somebody notices a report did not arrive — building the error reporting that fixes that is one of your first real projects.
- Diagnosing failures that do not announce themselves. Our PSA will happily return zero rows, or every row, without throwing an error. You will learn to distrust anything marked "successful."
- Building new automation from a prioritized list of things that currently eat someone's afternoon every week.
- Supporting our client portal and, later, the compliance and training product inside it. Our owner and Director of IT are launching that themselves — it comes to you around month six, once you have your footing.
- Looking after a growing set of small internal tools that our staff use daily and nobody currently owns.
- Writing down how all of it works, so that none of it depends on one person again — including you.
- Credential hygiene, tracking what we spend on AI, and keeping a clear record of what client data goes where.
What you need on day one
- Some MSP or IT service experience. You understand tickets, time entry, RMM and PSA data, and roughly how the business makes money. This is the context everything else sits on and it is slow to teach from scratch.
- The troubleshooting instinct. You read error messages properly, change one thing at a time, and check against reality instead of assuming. If you have ever found a bug everyone else had written off as "just how it is," lead with that story.
- Evidence you taught yourself something nobody asked you to. A PowerShell script, a spreadsheet that got out of hand, a Zapier flow, a home lab. We do not care what it was. We care that the impulse is there.
- Reliability with recurring work that has a deadline. Part of this job is a compliance publishing cycle that runs on someone else's schedule and cannot slip. If precise repetition drains you, this will wear badly.
- Willingness to be corrected a lot for six months, and to find that useful rather than annoying.
- Clear writing. Enough that someone can follow your documentation without asking you.
- The ability to work remote without supervision — and to say something early when you are stuck instead of going quiet.
- Trustworthiness. This role eventually holds administrative access across every client environment we manage.
Compensation and logistics
- $65,000 to $80,000 base, depending on your MSP depth and what you have already taught yourself.
- Formal performance reviews at month three and month six against the ramp deliverables above, then annually.
- Full\-time, exempt, with our standard benefits package.
- Fully remote within the United States. We do need you US\-based: this role eventually holds administrative access to systems containing our clients' patient data, and our insurance and client agreements require that access stays domestic. It is not about trust — it is about what we can defensibly tell a dental practice during an audit.
- No on\-call rotation during your ramp. If we add one later, we will pay for it and discuss it with you first.
Hours: 10:00am to 7:00pm EST/8:00am to 5:00pm MST
Salary Context
This $65K-$80K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Darkhorse Tech, 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. This role's midpoint ($72K) sits 66% below the category median. Disclosed range: $65K to $80K.
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.
Darkhorse Tech AI Hiring
Darkhorse Tech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $80K - $80K.
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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