Interested in this AI/ML Engineer role at Centerpointe Research Institute?
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About This Role
Department: Marketing \| Reports To: Marketing \& JV Manager \| Location: Hillsboro, OR (Full\-Time, On\-site) Schedule: Monday through Friday, 8:30am to 5pm Pay: $20 to $23/hour, based on experience
If you've been quietly rebuilding your own workflows with AI for the past year, this job is for you.
Most companies are still figuring out what "AI implementation" even means. We're past that. We need someone who already treats AI as a creative partner, who gets a genuine kick out of finding the workflow nobody's automated yet, and who wants to bring that energy to a marketing team that's ready to move fast.
Position Overview
Centerpointe Research Institute, founded in 1989, is a leader in personal development technology, reaching hundreds of thousands of people worldwide through our flagship Holosync brainwave entrainment program. We're a lean, fast\-moving direct\-response marketing team where email is our primary revenue channel, and every send matters.
The job has changed, fast. A year ago, this role was about manually building emails, cleaning lists, and pulling reports by hand. AI now handles a lot of that. What it can't do is decide what to build, judge whether the output is actually good, or connect the dots across five different tools and platforms. That's the job now, and it's a genuinely fun one if you're wired for it.
We're looking for someone who lives inside AI tools already: someone who has a favorite prompt technique, who's tried and abandoned three tools this year alone, and who talks about this stuff unprompted because they find it interesting. You'll set up and run AI\-powered workflows across email, CRM, reporting, and content, and you'll be the person who catches it when the AI gets something wrong. If you want a job where "what should we automate next" is a real question people ask you, not a buzzword on a slide, keep reading.
Key Responsibilities
AI Workflow Design \& Implementation
- Build and maintain AI\-assisted workflows for email production, campaign setup, and reporting across Keap and Mailchimp
- Evaluate new AI tools relevant to marketing ops and make the call on what's worth adopting
- Write and refine prompts, templates, and structured processes so AI output is consistently usable, not just fast
- Document workflows in Notion so they're repeatable and don't live only in your head
Quality Control \& Judgment
- Review AI\-generated emails, segments, and reports before anything goes live; catch errors humans would catch and AI won't
- Maintain list hygiene standards (bounce rates, spam complaints, deliverability) using tools like ZeroBounce, with AI doing the first pass and you doing the sanity check
- Make sure every email that goes out actually sounds like Centerpointe and serves the purpose it's meant to serve
Reporting \& Pattern Recognition
- Maintain dashboards pulling from multiple platforms, increasingly built and updated with AI assistance
- Use AI to surface patterns and flag anomalies in campaign data, then apply your own judgment on what's actually worth acting on
- Translate what the data is showing into plain language the rest of the team can use
Team Enablement
- Be the go\-to person for "can AI do this for us" questions from the rest of the marketing team
- Stay current on new tools and share what's working (and what isn't) with the team
- Help retire manual processes that AI has made unnecessary, and redirect that saved time toward higher\-value work
Qualifications
- Genuinely fluent in AI tools, not just aware of them; you use them daily and have opinions about which ones are actually good
- Comfortable with email marketing, CRM tools, or general marketing operations (or a fast learner who's picked up adjacent tools quickly)
- Strong judgment: you can tell when AI output is right, when it's almost right, and when it's confidently wrong
- Proficient in Google Sheets or willing to develop that skill quickly
- Strong written communication: you can explain what you're seeing in data, or what a workflow does, in plain language
- Self\-directed and comfortable proposing "here's what we should automate next" without being asked
What Sets the Right Candidate Apart
You don't need to have done this exact job before, because this exact job barely existed a year ago. You need to be the kind of person who's already treating AI as core to how you work, who gets curious about *why* an output is wrong instead of just fixing it and moving on, and who takes real satisfaction in building a system that quietly works well instead of doing the same task by hand every day.
What We Offer
- $20 to $23/hour, based on experience
- Full benefits package including Health, Dental, Vision, and PTO
- A collaborative, small\-team environment where your work has direct and visible impact
- A stable, established company with over 35 years in business and a mission you can get behind
Why Work at Centerpointe
Most marketing jobs ask you to sell things. This one asks you to help people change their lives.
Centerpointe's Holosync technology has helped over 2\.2 million people around the world reduce stress, sleep better, think more clearly, and show up differently in their lives. Our customers aren't just buyers; they're people who've had real breakthroughs. That's what's behind every email we send, and now it's what's behind every workflow we build.
If you care about personal growth, meditation, or the science of how the brain works, you'll find yourself genuinely connected to what we do here. And if you're the kind of person who wants their work to mean something beyond the metrics, this is a place where that's possible.
We're a small, tight\-knit team in Hillsboro, Oregon. People here tend to stay. There's a reason for that.
Pay: $20\.00 \- $23\.00 per hour
Benefits:
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
Work Location: In person
Salary Context
This $41K-$47K 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 Centerpointe Research Institute, 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 ($44K) sits 79% below the category median. Disclosed range: $41K to $47K.
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.
Centerpointe Research Institute AI Hiring
Centerpointe Research Institute has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Hillsboro, OR, US. Compensation range: $47K - $55K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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