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About This Role
Pattern is looking for a curious, analytical, and growth-minded AI Visibility Strategist to help more physicians discover Pattern when searching online or asking AI tools questions about financial planning and insurance.
This is an opportunity for someone who loves both strategy and execution. You won't just make recommendations and hand them off to someone else. You'll identify opportunities, build the plan, create and optimize content, test new approaches, and use data to understand what's working. You'll help Pattern grow its presence across traditional search engines and emerging AI platforms, turning that visibility into meaningful conversations with physicians.
What You Will Do
- Grow Pattern's visibility in AI search. Help Pattern show up accurately and consistently in AI-generated answers across ChatGPT, Perplexity, Gemini, and Google AI Overviews.
- Own organic search strategy. Determine which physician questions Pattern should answer, which pages need to be created or improved, and how to increase qualified leads through organic search.
- Turn expertise into valuable content. Work with Pattern's advisors and physician partners to capture their knowledge and turn it into credible, helpful content that search engines and AI platforms can understand, reference, and cite.
- Make our website work harder. Identify and address technical SEO issues, optimize pages, improve site structure, and ensure search engines can discover and understand our content.
- Track search performance, competitor activity, and emerging trends. Use data and experimentation to understand what drives qualified physician leads and meetings with advisors.
- Partner with Pattern's Systems Architect to develop repeatable processes, tools, and workflows that make organic and AI search efforts more effective and scalable.
- Connect the dots. Work with internal and external teams to turn insights into action and connect search activity to business outcomes.
- Search is changing quickly, especially as AI reshapes how people find information. Stay curious, test new approaches, and help Pattern adapt as the landscape evolves.
What We Are Looking For
- 5+ years of Search Engine Optimization (SEO) and content strategy experience, with a background in both hands-on execution and strategic planning.
- A proven track record of sustained organic growth, with measurable results showing how pages or programs have increased leads or revenue over time.
- A portfolio of SEO content you have written or directed.
- Proven technical experience optimizing websites for SEO and Answer Engine Optimization (AEO), including schema markup and structured data.
- Strong skills in keyword research, user-intent analysis, and strategic content planning.
- 1+ year of hands-on AI experience, including large language models (LLMs) and prompting, applied to real search or content work.
- Experience working with an agency or vendor partner toward clear performance goals.
- Hands-on experience with at least one AI search visibility tool, or a clear approach to tracking AI mentions and citations independently.
- Experience with SEO and digital marketing tools such as Google Search Console, Google Analytics 4, Semrush, Ahrefs, Screaming Frog, or similar platforms.
- Experience working in a content management system (CMS), such as WordPress.
- The ability to turn data into actionable recommendations and use A/B testing to validate what works.
- Strong writing and editing skills, with the ability to turn complex topics into clear, useful, and authoritative content.
- A data-driven mindset, with the ability to analyze performance, identify opportunities, test ideas, and make evidence-based decisions.
- Adaptable and resourceful, with the ability to navigate changing tools, technology, and best practices.
What Success Looks Like
- Increased qualified leads generated through organic search.
- More meetings with advisors that originate from organic search.
- Improved visibility, accuracy of representation, and citations across relevant AI search platforms for Pattern.
- Priority pages that earn meaningful rankings, citations, and conversions.
- Traffic and rankings matter, but they're not the finish line. The goal is to help the right physicians find Pattern and take the next step.
Why Join Us?
Compensation and Benefits:
This is a full-time, in-office position located in Omaha, Nebraska. This position is not remote or hybrid at this time. The ideal candidate lives in the Omaha-Lincoln metro area and is willing to work from the office.
Hours: Monday-Friday, 8:00 a.m.–5:00 p.m.
Salary Range: $115,000-$130,000 annually, depending on experience and qualifications.
We Offer:
- 401(k) with generous company match
- Health Savings Account contributions for qualifying insurance plans
- Flexible Spending Account
- Short- and Long-Term Disability benefits
- Paid Time Off (PTO) and volunteer days
- Sick Days
- Employee Assistance Program
- Company-paid Dental
- Vision Insurance Options
Pay: $115,000.00 - $130,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Employee assistance program
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Parental leave
- Retirement plan
- Vision insurance
Application Question(s):
- This position is in-office. Are you willing to work fully in office without the opportunity for remote or hybrid?
- Are you willing to undergo a background check, in accordance with local law/regulations?
Experience:
- Search Engine Optimization (SEO) and content strategy: 5 years (Required)
- AI experience, including large language models and prompting: 1 year (Required)
Location:
- Omaha, NE 68154 (Required)
Ability to Commute:
- Omaha, NE 68154 (Required)
Work Location: In person
Salary Context
This $115K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Pattern, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($122K) sits 42% below the category median. Disclosed range: $115K to $130K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Pattern AI Hiring
Pattern has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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