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AI\-Native Metadata Librarian
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Company: Third Iron, LLC
Position type: Full\-time
Location: Remote, US\-Based
About the Role
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Do you enjoy digging into complex bibliographic data, tracking down inconsistencies, and finding better ways to organize information at scale? Are you the person your colleagues turn to when metadata does not behave as expected—or when a large dataset needs to be understood, cleaned, or transformed? Do you enjoy using Excel, SQL, and AI tools to answer questions and develop more efficient workflows? If so, we would love to talk with you about joining Third Iron as an AI\-Native Metadata Librarian.
Since our remote\-first company's founding in 2011, Third Iron has created the industry\-leading software services BrowZine and LibKey. Our users include millions of students, faculty, doctors, researchers, and scientists affiliated with more than 2,000 libraries at universities, hospitals, corporations, and government departments across 36 countries—including organizations such as Stanford University, Mayo Clinic, NHS England, and the University of Hong Kong.
In this role, you will help improve and evolve the bibliographic data that powers Third Iron's systems and services. Working at the intersection of librarianship, cataloging, data analysis, and emerging technology, you will explore large\-scale bibliographic databases, investigate complex metadata issues, and translate your findings into practical improvements for our products, processes, and users. You will use Excel, SQL, and AI\-assisted tools while collaborating with technical and product teams to strengthen metadata quality, coverage, matching, and interoperability.
Responsibilities
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- Analyze, clean, reconcile, and enrich large\-scale bibliographic and holdings datasets.
- Investigate metadata quality, duplication, normalization, matching, and entity\-resolution issues.
- Use Excel, SQL, AI tools, and other data\-analysis methods to identify patterns, exceptions, and opportunities for improvement.
- Help design and refine metadata rules, mappings, validation processes, and automated workflows.
- Evaluate bibliographic data from publishers, aggregators, libraries, and other external sources.
- Support the evolution of Third Iron's metadata architecture, discovery capabilities, and knowledge\-base systems.
- Develop repeatable methods for assessing metadata completeness, accuracy, consistency, and timeliness.
- Document data sources, standards, workflows, decisions, and quality\-control procedures.
- Use AI\-assisted approaches thoughtfully while validating results and maintaining appropriate standards for accuracy, privacy, and responsible use.
Required Qualifications
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- At least 2 years of professional work experience in libraries, data science, metadata analysis or similar role.
- Master's degree in library and information science, a related field, or equivalent professional experience.
- Professional experience working with bibliographic metadata, library systems, discovery services, knowledge bases, or scholarly information.
- Strong working knowledge of Excel, including functions, filters, lookups, data transformation, and analysis of large datasets.
- High degree of comfort using AI tools for research, analysis, vibe coding mini apps, workflow development, and problem\-solving.
- Familiarity with bibliographic identifiers and metadata standards such as DOI, ISSN, ISBN, MARC, KBART, Dublin Core, or related formats.
- Strong analytical skills and the persistence to investigate ambiguous or incomplete data.
- Ability to explain metadata and data\-quality issues clearly to both technical and nontechnical colleagues.
- Excellent written communication, documentation, and remote\-collaboration skills.
- Experience with APIs, JSON, XML, regular expressions, scripting, or data\-transformation tools.
- Comfortable with reviewing metadata samples and designing tools to analyze data mechanically with AI.
- Ability to work independently, manage priorities, and contribute effectively within a distributed team.
Preferred Qualifications
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- Experience writing SQL queries to explore, join, validate, and analyze data.
- Familiarity with metadata supplied by publishers, content aggregators, link resolvers, or library vendors.
- Knowledge of entity resolution, record matching, deduplication, authority control, or identifier reconciliation.
- Familiarity with scholarly communication infrastructure and data sources such as Crossref, PubMed, OpenAlex, ORCID, or library knowledge bases.
- Experience evaluating or developing AI\-assisted metadata workflows.
- Experience collaborating with software engineers, product managers, or data teams.
- 1\+ year of success working in a remote position.
What “AI\-Native” Means at Third Iron
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We are looking for someone who treats AI as a practical part of modern knowledge work. An AI\-native candidate is eager to experiment, understands how to formulate and refine effective prompts, can use AI to accelerate analysis and technical work, and knows when human judgment and verification are essential. A strong candidate will have demonstrated direct experience applying AI to metadata or data\-quality challenges.
What Success Looks Like
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In this role, you will help Third Iron:* Improve the accuracy, consistency, and coverage of its bibliographic data.
- Identify systemic metadata issues that cannot be solved through one\-record\-at\-a\-time cataloging.
- Develop scalable, repeatable approaches to data analysis and quality control.
- Make better use of automation and AI while preserving professional standards and human oversight.
- Turn complex bibliographic data into more reliable and useful experiences for libraries and their users.
Working at Third Iron
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This is a remote position designed for someone who communicates proactively, works well independently, and enjoys collaborating across disciplines. You will have the opportunity to apply library expertise at scale and directly influence the data, systems, and services used by Third Iron's customers. Third Iron is a small company of around 30 employees and our growth and success have been fueled by the passionate, curious people who work here. We value input and feedback while looking to minimize the interruptions of meetings, report writing, and internal emails.
Understanding that work is just part of your life, Third Iron also provides benefits including health, dental, long\-term disability, paid time\-off, home\-office stipend, and more. It's why most employees who work at Third Iron spend many years of their careers here. You could, too!
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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 Third Iron, LLC, 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 in Demand for This Role
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.
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.
Third Iron, LLC AI Hiring
Third Iron, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
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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