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About This Role
About Us
Clayco is a full-service, turnkey real estate development, master planning, architecture, engineering, and construction firm that safely delivers clients across North America the highest quality solutions on time, on budget, and above and beyond expectations. With $8.1 billion in revenue for 2025, Clayco specializes in the "art and science of building," providing fast track, efficient solutions for mission critical, industrial, life sciences, power & energy, aviation, commercial, institutional, residential and sports & entertainment related building projects.
The Role We Want You For
Across Clayco, people have built their own AI tools to get through the document-heavy parts of their work — reviewing submittals, finding the right procedure, pulling history off a past project. Nobody asked them to. The work is tedious, the tools were available, and no one had given them a better option.
That appetite is both the opportunity and the problem. We run governed agents on a Snowflake semantic layer today, with far more of the work ahead of us than behind. The gap is between what people will build for themselves and what we can actually stand behind.
You will close it. This role builds the AI applications Clayco employees use in their daily work — governed, supported, and good enough that the private versions stop being worth the trouble.
The Specifics of the Role
- Build governed AI applications that make the ad-hoc tools people built for themselves unnecessary
- Build retrieval over our internal knowledge base: procedures, training content, project history, platform documentation
- Turn our domain model into working retrieval boundaries rather than a diagram
- Evaluate model and approach choices against real Clayco work rather than public benchmarks
- Set the prompt and context engineering standards the engineers after you will inherit
- Build inside our agent registry and identity model, not around it
Requirements
- You have shipped this to real users. Retrieval and document intelligence in production, with people depending on it; not a prototype that impressed a steering committee.
- You evaluate honestly. You know whether a system works because you tested it on real tasks and looked at where it failed. If you have ever shipped something you knew was not ready, you know why this matters.
- You can work with the people who do the work. Sitting with a project engineer, watching them review a submittal, and understanding why the obvious automation would be wrong.
- You are comfortable early. This pillar is being built. You will have architectural latitude and fewer people to ask than you would somewhere established.
- Nice to have: construction or AEC background, Snowflake Cortex, n8n, document extraction at scale, experience consolidating shadow tooling into something supported.
Some Things You Should Know
- Our clients and projects are nationwide – Travel will be required.
- No other builder can offer the collaborative design-build approach that Clayco does.
- We work on creative, complex, award-winning, high-profile jobs.
- The pace is fast!
- This position is classified as a safety-sensitive role in accordance with applicable state and federal laws. Candidates selected for this position will be subject to a comprehensive background check, which includes mandatory drug testing.
Why Clayco?
- 2025 Best Places to Work – St. Louis Business Journal, Los Angeles Business Journal, and Phoenix Business Journal.
- 2025 ENR Top 400 – Top Data Center Contractor (Top 3).
- 2025 ENR Top 100 Design-Build Firms – Design-Build Contractor (Top 5).
- 2025 ENR Top 100 Green Contractors – Green Contractor (Top 3).
Benefits
- Discretionary Annual Bonus: Subject to company and individual performance.
- Comprehensive Benefits Package Including: Medical, dental and vision plans, 401k, generous PTO and paid company holidays, employee assistance program, flexible spending accounts, life insurance, disability coverage, learning & development programs and more!
Compensation
- The salary range for this position considers a wide range of factors in making compensation decisions including but not limited to: Education, qualifications, skills, training, experience, certifications, internal equity, and location. Compensation decisions are dependent on the facts and circumstances of each case.
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 Clayco, 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.
Clayco AI Hiring
Clayco has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in St. Louis, MO, 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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