Interested in this AI/ML Engineer role at Fireclay Tile?
Apply Now →Skills & Technologies
About This Role
Fireclay Tile is here to revolutionize the tile and design industry. By implementing an innovative digital platform paired with unmatched artisan-crafted products and client service, we've made it easier than ever to specify and purchase beautiful, sustainable tile sourced and handcrafted in the US. Our tireless commitment to innovation, transparency, and sustainability has not gone unrecognized. We aim to grow 25-35% annually and are the first tile company recognized as a B Corp for how we use Business as a Force for Good. With an innovative product, an ambitious team, and an inspiring mission, we're changing the way you think about tile. Come join the makers who support the dreamers!
Location: Northern California preferred
Team: Two developers, with capacity to build the team over time
Compensation: 160K-180K Base The Team You'll Work With
You will report to Fireclay's VP of Technology, Dave Meagher, and partner closely with both the VP of Technology and our CEO to shape the next version of Fireclay's technology: a company that runs on AI, end to end.
You will lead a small team of developers building across Salesforce, Google Cloud, Shopify, and our Snowflake data layer. The VP of Technology owns the broader roadmap. You will own the AI rollout across every team, the hands-on engineering leadership, and the applied-AI work that turns that roadmap into durable business value. The Challenge
Fireclay is AI-forward, and we want someone to own the AI rollout across the entire company, from the factory floor to marketing to sales.
We envision a near future where every knowledge worker at Fireclay uses AI all day long, as naturally as email. Production planners, customer service, sales reps, designers, marketers, and finance should each have AI woven into how they work, backed by systems they trust. Getting there is your job.
Fireclay Tile has always used technology to do things differently. We were early to the cloud, and over the last few years we have gone well past experimentation: we now run production AI skills, agents, and internal applications that our teams use every day.
We are a vertically integrated, employee-owned B Corp that makes beautiful, sustainable tile and helps clients carry a project from inspiration through installation. The work is real: complex product data, factories, supply chain, sales, design, customer service, showrooms, installation, finance, and thousands of decisions that need to happen well every day.
Owning AI here means owning more than tools. You will define and run the enterprise architecture, security, governance, and process controls that let AI scale safely across every team. That includes how models and agents access data, who can do what, how work is reviewed, how we audit and observe it, and how we decide what gets built, bought, scaled, or stopped.
This is not a role for an AI futurist who stays at 30,000 feet. You will be in the weeds: mapping work, finding constraints, building solutions, coaching teammates, and holding a high bar for security, quality, and measurable impact. How We Work
We run our technology team like a software startup inside a manufacturing company. The pace is fast, the team is lean, and shipping matters more than planning decks.
In exchange, you get a lot of autonomy. You will have the room to learn the business deeply, decide where technology and AI can add value, and act on it quickly without waiting on layers of approval.
With AI, the distance between an idea and a working application is now days, not quarters. It is normal here to scope a problem with a business owner on Friday, build the application with AI over the weekend, and put it in front of real users on Monday. We want someone who finds that energizing, and who knows how to move that fast without cutting corners on security, data integrity, or review. Our Stack
Claude, MCP, custom skills and agents, Google Cloud, Salesforce, Shopify Plus, Front, Dialpad, Slack, Hyros, HubSpot The Opportunities You'll Have* Own the AI rollout across every Fireclay team, from production and supply chain to customer service, marketing, sales, and finance.
- Partner with the VP of Technology and the CEO to define the next vision of Fireclay's technology, built on AI.
- Design and run the enterprise architecture for AI: how agents, skills, and applications connect to Salesforce, Google Cloud, Shopify, and Snowflake, and how data flows between them.
- Own AI security, governance, and process control: access and permissions, data privacy, human review, audit trails, observability, and clear ownership for every production workflow.
- Lead, mentor, and grow Fireclay's development team, and raise the bar for how we build.
- Learn Fireclay deeply, from customer journey and order flow through manufacturing, fulfillment, installation, and support, and find the real points of friction, delay, rework, and opportunity.
- Build and ship AI-enabled workflows and applications quickly, often in days, that improve productivity, decision quality, customer experience, revenue, or cost.
- Identify and pursue customer-facing AI opportunities that make it easier to discover, design, purchase, receive, and install Fireclay products.
- Run a disciplined intake and prioritization process. Every meaningful AI investment should have a defined problem, baseline, owner, cost, expected value, success metric, security review, and scale-or-stop decision.
- Make the right build-versus-buy decisions. Use external tools, partners, and emerging models where they create leverage, without creating a fragile or ungoverned stack.
What Success Looks Like Early
- First 30 days: Map Fireclay's stack across Salesforce, Google Cloud, Shopify, and Snowflake, along with current AI use, developer capacity, core data flows, security posture, and highest-friction business processes. Meet the people doing the work, including in the physical operation. Ship something small and useful.
- First 60 days: Present an AI rollout plan for every team, an enterprise AI architecture and governance framework, a capability assessment, and a prioritized 12-month plan. Select two or three high-value use cases with clear owners, baselines, ROI hypotheses, and delivery plans.
- First 90 days: Ship at least one secure, measurable production workflow or application. Put in place an AI investment scorecard, a teammate enablement rhythm, a mentoring plan for each developer, and the technical standards required to scale responsibly.
What Success Looks Like in the First Year You will have created visible, credible impact, not a slide deck of possibilities.
That means every team is using AI daily in its core work, backed by governed, secure systems. It means measurable cost savings, productivity gains, better decisions, faster developer throughput, or new revenue. It also means our developers have grown noticeably under your leadership, and Fireclay has a clear, shared architecture for an AI-first future.
We will care about token spend, but not in isolation. Every meaningful investment should show a clear return or a credible, measurable path to one. About You* You are a builder. You have personally written and shipped production code, and you still want to. You use AI coding tools every day and can take an application from idea to production in days.
- You thrive at startup speed. You are energized by autonomy, comfortable with ambiguity, and bias toward shipping and learning.
- You have deep experience with modern AI application development: agents, MCP and tool use, automations, model selection, evaluation, retrieval, prompt and workflow design, and observability.
- You have designed enterprise architecture, security, and governance for AI or data systems. You know how to give teams powerful tools while controlling access, protecting data, and keeping humans in the loop where it matters.
- You have hands-on experience across a stack like ours: Salesforce, Google Cloud, Shopify, and a cloud data warehouse such as Snowflake. You care more about how systems connect than about any single platform.
- You are a strong mentor to junior developers. You have grown engineers early in their careers through code review, pairing, clear standards, and stretch assignments. You teach them to use AI well, to understand the business behind the ticket, and to own their work end to end. Your team gets better because you lead it.
- You can lead developers while staying close enough to the work to make good technical and product decisions.
- You bring conviction and humility: strong standards, direct communication, curiosity, and a willingness to change your mind when the evidence changes.
The Hiring Conversation
We want to see how you think and how you work, not just what is on your résumé.
Our VP of Technology will share a short video showing how he uses AI and how Fireclay applies it today. As part of the application, every candidate will submit a 3–5 minute Loom or screen recording showing one real way they use AI in their workflow.
Show us the problem you were solving, the tools and inputs you used, what you built or changed, how you checked the work, and the result. Bonus points if it's something you built fast. A sanitized example is welcome; do not share confidential customer, employer, or personal information. We are looking for real judgment and practical leverage, not AI theater. Why Fireclay?
Beyond the role itself, here’s what makes Fireclay Tile a special place to work:
- Our Mission: We are on a mission to do good by crafting products intentionally, collaboratively, and transparently. We are the makers, who support the dreamers.
- Our Values: We live by Kindness, Honesty, Tread Lightly on the Earth, Measure Twice, Cut Once, and Ganas (Desire).
- Our Commitment: We are accountable to the B Corp standards, prioritizing People and Planet alongside Profit.
- Growth and Innovation: We are committed to "25-35% annual growth" and are revolutionizing the tile and design industry.
- Our Culture: We foster a culture of professional development, ongoing training, and opportunities for growth.
- Our Benefits: We offer a comprehensive compensation package, including medical, dental, vision, a 4% 401(k) match, PTO (non-exempt) or FTO (exempt), sick time, and other perks. Visit Fireclaybenefits.com to view details of our benefits and perks offered.
Serving as a Fireclay Tile Brand Ambassador, representing our core values including: Kindness Honesty Tread Lightly on the Earth Measure Twice, Cut Once Ganas (Desire) Accountable to the B Corp standards, we take care of our People and our Planet while turning a Profit.
The total compensation package for this position may also include other elements, stock options, and discretionary bonus. Details of participation in these benefit plans will be provided if a teammate receives an offer of employment.
Fireclay provides equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, pregnancy, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, Fireclay will ensure that persons with disabilities are provided reasonable accommodations. If you need a reasonable accommodation, please let us know by contacting [email protected].
Salary Context
This $160K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Fireclay Tile, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Fireclay Tile AI Hiring
Fireclay Tile has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Davis, CA, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 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).
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 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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.