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About This Role
Prompt EngineerAbout the Role: We're looking for a skilled Prompt Engineer to join our AI team and shape how our products communicate with large language models. You'll sit at the intersection of linguistics, engineering, and product — crafting the instructions that make AI genuinely useful. You have a genuinely curious, inventive mindset — you explore model behavior with a habit of probing edge cases, questioning assumptions, and regularly surfacing insights the team didn't know to look for.
As a Prompt Engineer, you will design, test, and iterate on prompts that drive our AI\-powered products. Working alongside product managers, engineers, and designers, you'll be responsible for the quality, consistency, and safety of all LLM outputs — from customer\-facing features to internal automation pipelines.
Responsibilities:* Partner with product, engineering, and design to rapidly prototype and iterate on prompts for production LLM applications across diverse use cases. Turn around prompt revisions within tight cycles, maintaining quality without sacrificing speed
- Test and iterate on prompts systematically to improve accuracy, consistency, speed and output quality
- Build prompt templates and libraries that enable reusable, scalable AI solutions across the organization
- Document prompt patterns, techniques, and findings for team knowledge sharing
- Evaluate AI model outputs against quality criteria and implement improvement strategies
- Collaborate with engineers to integrate prompts into scalable, reliable pipelines
- Monitor model behaviour post\-deployment, implement versioning and A/B testing strategies and respond to regressions or edge cases
- Research emerging prompting techniques and apply them
- Contribute to red\-teaming and safety reviews to identify risks in LLM outputs
Requirements:* 2\+ years of hands\-on experience in prompt engineering or a closely related AI/NLP role with significant focus on working with LLMS and Generative AI; demonstrated expertise in prompt engineering
- Hands\-on experience with tool\-calling patterns, function routing, and structured output formatting in the context of agent orchestration
- Systematic testing and experimentation methodologies and iteration
- Creative problem\-solving to find innovative solutions using AI
- Understanding of token economics, context windows, and model limitations
- Strong written communication skills — clarity and precision are your superpowers
- Ability to document processes and communicate technical findings to non\-technical stakeholders
- Comfortable working in\-office five days a week in a fast\-paced, collaborative environment
Nice to Have
- Prior work in a product\-led or SaaS environment
- Experience collaborating with clinicians, medical writers, or editorial teams in a publishing setting, translating domain expertise into precise, compliant LLM instructions
At Internet Brands, we carefully consider a wide range of factors when determining compensation, including your background and experience. These considerations can cause your compensation to vary. We expect the compensation for this role to range from $60k \- $105k and depend on your skills, qualifications, and experience. We encourage all interested candidates to apply.
In addition to our awesome culture, we offer a comprehensive benefits package designed to support the health and well\-being of you and your family. Our benefits include health insurance options such as medical, dental, and vision coverage, flexible spending accounts (FSA) for medical and dependent care, short\-term and long\-term disability insurance, and life and AD\&D insurance. We also provide a 401(k) retirement savings plan with a company match, paid time off (PTO), paid holidays, commuter benefits as well as access to our Employee Assistance Program (EAP) and well\-being coaching services. In addition, employees can take advantage of voluntary benefits such as home, auto and pet insurance, and discounted legal and financial services. For more details, feel free to inquire during the interview process.
Internet Brands®, headquartered in El Segundo, Calif, is a fully integrated online media and software services company focused on four high\-value vertical categories: Health, Automotive, Legal, and Home/Travel. The company's properties and platforms include the WebMD, Medscape, and Henry Schein ONE networks, which are the global leaders in their markets; Nolo, Avvo, and Martindale, which form the largest consumer information provider in the legal market; and CarsDirect, Fodor's Travel, and many others which are leaders in their key vertical markets. Internet Brands' award\-winning consumer websites lead their categories and serve more than 250 million monthly visitors, while a full range of web presence offerings has established deep, long\-term relationships with SMB and enterprise clients. The company's powerful, proprietary operating platform provides the flexibility and scalability to fuel the company's continued growth. Internet Brands is a portfolio company of KKR and Warburg Pincus.
Internet Brands is an equal opportunity employer, and we welcome applicants from all backgrounds. We comply with all applicable laws and regulations, including the City of Los Angeles Fair Chance Initiative for Hiring Ordinance (FCIHO).
Notice to California residents: you can find information about our privacy practices, on: https://www.internetbrands.com/work\-with\-us/cpra
Role Details
About This Role
Prompt Engineers design, test, and optimize interactions with large language models. They build evaluation frameworks, craft system prompts, and develop techniques like chain-of-thought and few-shot learning to get consistent, reliable outputs. The role emerged alongside the GPT-3 era and has matured into a legitimate engineering discipline, not the 'just talk to the AI' job that early skeptics dismissed.
The work is more systematic than creative. You're running hundreds of prompt variations through evaluation suites, measuring output quality across edge cases, and building guardrails for production systems. When a prompt works 95% of the time but fails catastrophically on the other 5%, you need to find those failure modes and fix them before they hit users.
Across the 3,824 AI roles we're tracking, Prompt Engineer positions make up 0% of the market. At Internet Brands, this role fits into their broader AI and engineering organization.
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
What the Work Looks Like
A typical week involves designing evaluation datasets for new use cases, benchmarking prompt strategies against each other with statistical rigor, working with product teams to define 'good enough' output quality, and building the tooling that lets non-technical teammates iterate on prompts safely. You'll spend more time in spreadsheets and evaluation dashboards than you'd expect.
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
Skills Required
The core requirement is deep LLM experience: prompt design, RAG architectures, and evaluation methodology. Python is table stakes. Many roles also want experience with specific providers like OpenAI, Anthropic, or open-source models. Understanding tokenization, context windows, and the practical differences between model families (reasoning ability, instruction following, output format compliance) separates strong candidates from the crowd.
Evaluation skills are becoming the differentiator. Can you design a rubric that measures output quality? Can you build automated evaluation pipelines? Do you understand when to use human evaluation vs. LLM-as-judge vs. deterministic checks? Companies are moving past 'vibes-based' prompt testing and want engineers who bring measurement discipline.
Strong postings specify the LLM use cases (summarization, extraction, classification, generation), the evaluation methodology they expect, and the production environment. Weak postings just say 'prompt engineering experience' without context. Look for companies that mention evaluation frameworks and production deployment.
Compensation Benchmarks
Prompt Engineer roles pay a median of $142,800 based on 14 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($82K) sits 42% below the category median. Disclosed range: $60K to $105K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Internet Brands AI Hiring
Internet Brands has 1 open AI role right now. They're hiring across Prompt Engineer. Based in El Segundo, CA, US. Compensation range: $105K - $105K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into Prompt Engineer roles include Technical Writer, NLP Researcher, Software Engineer.
From here, career progression typically leads toward AI Product Manager, LLM Engineer, AI Solutions Architect.
The best prompt engineers come from technical backgrounds and add LLM expertise, not the other way around. If you're coming from a non-technical role, invest heavily in Python, evaluation methodology, and understanding how LLMs work under the hood (tokenization, attention, context windows). The role will increasingly merge with LLM Engineering as the tools mature.
What to Expect in Interviews
Interviews focus on evaluation methodology and systematic thinking. You'll likely be asked to design a prompt for a specific use case, explain how you'd measure output quality, and walk through how you'd debug a prompt that works 90% of the time but fails on edge cases. Expect to discuss tokenization, context window management, and the tradeoffs between different prompting strategies (few-shot vs. chain-of-thought vs. tool use).
When evaluating opportunities: Strong postings specify the LLM use cases (summarization, extraction, classification, generation), the evaluation methodology they expect, and the production environment. Weak postings just say 'prompt engineering experience' without context. Look for companies that mention evaluation frameworks and production deployment.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Prompt engineering roles are still growing but the market is maturing. Early roles were broad and experimental. Now, companies know what they want: someone who can systematically improve LLM output quality, reduce costs by optimizing token usage, and build evaluation infrastructure. The roles that survive will be the ones that look more like engineering than copywriting.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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