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About This Role
Description: Build your career with Heritage Auctions, the world’s largest collectibles auctioneer.
Celebrating its 50th anniversary in 2026, Heritage Auctions has spent five decades connecting collectors with extraordinary objects and the stories behind them. Headquartered in Dallas, Texas, with offices across the United States and around the world, Heritage serves more than 50 auction categories, including Coins, Sports, Comics, Entertainment, Historical, Jewelry, Fine and Decorative Art, Luxury Accessories, Wine, and many more. Join a collaborative, fast-paced company where curiosity is valued, expertise is celebrated, and every day offers an opportunity to work with remarkable pieces of history and culture.
SUMMARY:
Heritage Auctions is seeking a Search & AI Discovery Architect to make Heritage’s information easier for people and AI systems to discover, understand, trust, and cite across traditional search engines, AI-powered search, answer engines, assistants, and emerging agent ecosystems.
This role combines deep search knowledge with hands-on technical architecture and experimentation. You will own the technical designs, information and relationship models, standards, feasibility assessments, dependencies, implementation guidance, and validation that support Heritage’s search and AI-discovery strategy. You will personally build prototypes and reference implementations, then partner with Engineering to turn validated approaches into production capabilities.
As part of our Enterprise AI team, reporting to the Manager, Enterprise AI, you will work closely with the SEO and AEO Strategy Manager, who owns commercial strategy, growth priorities, the delivery backlog, and business outcomes. You will advise and influence the growth roadmap through technical evidence and emerging capabilities.
Success means making Heritage a more accessible and trusted source for auctions, collectibles, valuations, provenance, historical objects, and market intelligence while preserving the meaning and accuracy of its information. In partnership with the SEO and AEO Strategy Manager, you will support improved visibility and citations for strategic topics and a measurable contribution to qualified traffic, registrations, consignments, bidding activity, purchases, and revenue
BENEFITS:
- Medical, Dental, Vision coverage
- Paid time off
- 401k savings plans
- Onsite gym with access to a personal trainer
COMPENSATION: Commensurate on experience
LOCATION: South of Dallas/Fort Worth International Airport, NW corner of W. Airport Freeway (Hwy. 183) and Valley View Lane
KEY RESPONSIBILITIES:
- Define, document, and govern technical standards for crawlability, indexing, rendering, canonical URLs, metadata, internal linking, structured data, content architecture, and search performance. Maintain reusable implementation patterns that reduce ambiguity and repeated research. Assess how Heritage’s inventory, archives, specialist expertise, and market data are exposed to search engines and AI systems, and identify barriers to accurate discovery, retrieval, attribution, and citation.
- Develop information models, reusable schema patterns, and knowledge and relationship graphs that distinguish an object from its auction appearances, an active offering from a historical auction result, and an estimate from a realized price. Preserve provenance, relevant dates, price definitions, and reliable links between records. Work with Heritage specialists, Data Services, and Engineering to validate entity definitions, attributes, identifiers, and relationships. Validate that discovery outputs preserve these
distinctions.
- Translate business priorities into clear technical designs, requirements, dependencies, tradeoffs, and acceptance criteria for Product and Project Management, Web, Data Services, and Engineering. Provide practical implementation guidance and test cases, surface risks and unresolved dependencies early, and help resolve technical gaps through production delivery and validation against intended behavior
- Personally build prototypes and reference implementations using Heritage data to test technical approaches, expose assumptions, and demonstrate proposed standards. Run focused experiments, compare results, and iterate before recommending broader adoption.
- Evaluate APIs, data feeds, agent access, Model Context Protocol (MCP) capabilities, and emerging discovery approaches, providing practical guidance on security, data availability, feasibility, and integration. Track evolving schema practices, naming conventions, and changes in how search engines and AI agents discover and interpret information. Use experimental evidence to explain their practical implications and proactively surface capabilities and constraints that should inform or reshape planning while following agreed business priorities.
- Establish technical and discovery baselines with the SEO and AEO Strategy Manager and relevant analytics partners. Measure and validate implemented improvements for information accuracy, crawlability, index coverage, structured-data quality, and accessibility to search and AI agents. Collaborate on measurement of visibility, citations, qualified traffic, conversions, and agreed business outcomes, distinguishing technical performance from commercial results.
- In your first 90 days, work with the SEO and AEO Strategy Manager, Engineering, Data Services, Web, and Heritage specialists to establish discovery, technical-accessibility, and information-accuracy baselines for an agreed representative scope. Identify the most consequential barriers and dependencies, agree on a representative category’s information model and pilot acceptance criteria, and build and test a reference implementation. Deliver a measurable implemented pilot with Engineering and validate results against the baselines and acceptance criteria, including information accuracy and accessibility to search and AI
agents. Document results, remaining limitations, and recommendations for the next business-prioritized improvements
Requirements: QUALIFICATIONS:
- Experience in technical SEO, web architecture, digital marketing technology, organic growth, or a related discipline, with strong knowledge of crawlability, indexing, website structure, metadata, canonicalization, and search performance.
- Broad technical understanding of front-end websites, back-end systems, structured data, APIs, data flows, and AI-discovery integrations.
- Ability to build and test prototypes and reference implementations and translate them into practical implementation guidance.
- Ability to model entities and relationships clearly and preserve the meaning of complex information across systems.
- Familiarity with AI-powered search, large language models, answer engines, and emerging discovery practices.
- Ability to translate business and marketing objectives into technical requirements and explain capabilities, constraints, and tradeoffs to technical and nontechnical partners.
- Experience partnering with developers, data engineers, product or project managers, marketers, and business stakeholders.
- Strong analytical, research, communication, and problem-solving skills, including the judgment to evaluate unfamiliar technologies and recommend a practical path forward.
- A strong sense of ownership for technical outcomes, including proactively identifying opportunities, surfacing risks, resolving gaps, and following work through validation.
PREFERRED EXPERIENCE:
- Experience implementing or guiding schema, structured-data, knowledge-graph, or entity-modeling initiatives.
- Familiarity with answer engine optimization (AEO), generative engine optimization (GEO), entity authority, or LLM visibility measurement.
- Working knowledge of JavaScript-rendered websites, content management systems, APIs, data feeds, and modern web architecture.
- Familiarity with agent integrations, MCP, or other methods for making organizational content available to external AI systems.
- Experience with Google Analytics, Google Search Console, Semrush, Ahrefs, Adobe Analytics, Salesforce, Robohead, or similar platforms.
- Experience with marketplaces, ecommerce, luxury goods, auctions, collectibles, or other expertise-driven industries
Heritage Auctions welcomes and encourages applications from women, minorities, veterans, mature workers, and persons with disabilities. EOE
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 Heritage Auctions, 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.
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.
Heritage Auctions AI Hiring
Heritage Auctions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
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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