Capitole is actively hiring for 2 AI and machine learning positions, primarily in Prompt Engineer (2) roles. Mid-level roles account for 100% of openings.
Open Positions (2)
AI Engineer | Prompt Engineering
AI Engineer Prompt Engineering
What Capitole's hiring tells you
With 2 active AI role(s), this company is in the early exploration phase. That can mean either a pilot project being staffed up or a small embedded AI function inside a larger team. Worth investigating directly: ask the recruiter how the AI work is funded and who it reports to. Compensation is not disclosed in postings, which is increasingly out of step with how AI talent expects to be hired.
Questions worth asking in the Capitole interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- Is this AI work funded for at least 18 months, or is it tied to a specific project deadline?
- Will I be the only person doing this, or are there others I will collaborate with day to day?
- What does success look like at six months? At eighteen months?
Capitole AI and ML Hiring
Capitole has 2 active AI and ML roles in our dataset. They're focused on Prompt Engineer hiring.
Salary Benchmarks
The market median for AI roles is $213,400. Prompt Engineer roles pay a median of $141,440 across the market. Top-quartile AI compensation starts at $264,230.
Skills in Demand
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.
AI Role Categories
Prompt Engineer
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 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.
Market compensation for Prompt Engineer roles: $141,440 median across 27 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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.
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.
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.
Skills in Demand for This Role
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.
Frequently Asked Questions
Frequently Asked Questions
Related Resources
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