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About This Role
About Us
Joseph Design Build is a multi\-award\-winning luxury design\-build firm based in Austin, Texas. Founded by Thomas and Gabe Joseph in 2014, we specialize in modern, function\-forward luxury homes of unrivaled craftsmanship and quality. Each residence is site\-specific and a carefully considered response to the way Austin's most discerning residents live, work, and connect.
Our vertically integrated team of architects, interior designers, engineers and project managers operates under one roof, ensuring seamless collaboration and a cohesive experience from first sketch to final walkthrough.
The Role
As our dedicated AI\-Native Marketing Lead, you will sit at the center of how Joseph Design Build communicates with prospective clients, real estate partners, and the broader Austin market.
This foundational role will report directly for our Chief Digital Officer, who brings over 15 years of experience in big technology working closely with advanced AI, product development, and mature brand marketing for companies like Wayfair, Shopify, \& Intuit Quickbooks. You will also work closely with our partners, architects, interior designers, and the marketing \& sales team to translate our projects and vision into compelling narratives that generate leads and deepen brand loyalty.
This role blends brand storytelling, content creation, marketing strategy, campaign management, event and home tour execution, marketing technology, and digital operations. It is ideal for someone who wants true ownership and autonomy — someone who uses AI not as a novelty, but as a force multiplier. You don't just prompt AI tools; you have experience working with tools like Zapier/Make, Hubspot, Claude, Airtable, and more to produce high quality marketing and automate routine work.
Responsibilities
Brand Storytelling \& Content
- Leverage AI tools to increase content velocity without sacrificing the premium aesthetic our brand demands.
- Tell the story of each project from groundbreaking to delivery: social posts, email campaigns, landing pages, project one\-pagers, and press pitches.
- Develop a distinct and consistent brand voice that reflects the quality, craftsmanship, and design intelligence at the heart of every Joseph home.
- Partner with our architects and interior designers to translate technical excellence into accessible, inspiring narratives for a high\-net\-worth audience.
Social Media \& Digital Presence
- Own and grow our social media presence across Instagram, TikTok, YouTube, and emerging platforms — building on our established following with intentional, design\-forward content.
- Maintain a strategic content calendar aligned with project milestones, home launches, design features, and industry moments.
- Monitor trends in luxury real estate, architecture, and design to keep our content timely and culturally resonant.
- Use AI tools to analyze engagement data, surface patterns, and continuously refine our content strategy.
Campaigns \& Marketing Operations
- Build and manage campaigns in HubSpot, Aloware, etc including newsletters, lifecycle sequences, segment\-based drips, and automations for buyers, brokers, and partners.
- Develop and execute lead generation campaigns targeting high\-net\-worth individuals, luxury real estate brokers, and design\-forward buyers in the Austin market.
- Create tailored collateral — presentations, lookbooks, project profiles — that support our sales team and founders in converting prospects.
- Build the systems and processes that will scale as the firm grows.
Events, Home Tours \& Brand Activations
- Define and execute Joseph Design Build's event strategy: open houses, private client dinners, home tours, design industry events, and sponsorships.
- Partner with founders and designers to create immersive, on\-brand event experiences that convert interest into conversations.
- Amplify all events externally through polished social content, email follow\-ups, and post\-event press materials.
- Represent the firm at select Austin\-area events, industry gatherings, and luxury lifestyle activations.
Insights \& Automation
- Act as our internal AI evangelist — continuously vetting new LLMs, image generators, and automation tools to keep our marketing at the leading edge of efficiency.
- Manage \& create new marketing automations in tools like Zapier; we don’t believe in doing routine, predictable tasks manually – automation is table stakes.
- Conduct structured conversations with clients, prospects, and real estate partners to surface insights that inform positioning and campaign strategy.
- Translate learnings into external content and internal recommendations that sharpen our message and deepen market understanding.
Who You Are
- An AI\-native operator: you have an AI\-first instinct for problem\-solving and are obsessed with building workflows that multiply your output; you have ready examples of how you use AI in your daily work to get outsized results
- A creative generalist with 4–6 years of marketing experience, ideally with exposure to luxury real estate, architecture, interior design, or a premium lifestyle brand (not required)
- Proficient in HubSpot, with hands\-on experience running email campaigns and lifecycle automations
- Comfortable in Adobe Creative Suite, Figma, or similar — you will have a design partner but should be able to competently use these tools
- A strong visual and written communicator with a portfolio that demonstrates your ability to craft compelling content and campaigns
- Passionate about design, architecture, and the luxury market — you understand the aesthetic sensibility of the audience we serve
- Strategic and data\-driven, with a track record of campaigns that deliver measurable results
- Highly organized, self\-motivated, and capable of managing multiple projects in a fast\-moving environment
- Based in Austin, TX
The Selection Process
We move fast and value proof of work. Candidates selected to move forward will be asked to complete a brief take\-home assignment and are expected to leverage AI. We want to see not only your marketing instincts — we want to see how you use modern tools to produce high\-quality, creative results quickly and efficiently.
After reviewing assignments, leading candidates will be invited for 3 rounds of technical, strategy, and behavioral rounds in which we aim to learn more about your working style and problem solving abilities.
Benefits
- Hybrid / remote work (3 days in office)
- Competitive salary \& performance bonus
- Healthcare, dental, \& vision coverage
- 2 weeks PTO, company holidays, opportunities for additional time off
- Team events \& company offsites
- The opportunity to shape the marketing identity of one of Austin's most celebrated design\-build firms
Pay: $75,000\.00 \- $108,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Flexible schedule
- Health insurance
- Paid time off
- Vision insurance
Experience:
- AI and automation: 2 years (Required)
Work Location: Hybrid remote in Austin, TX 78718
Salary Context
This $75K-$108K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Joseph Design Build, Inc., 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($91K) sits 45% below the category median. Disclosed range: $75K to $108K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Joseph Design Build, Inc. AI Hiring
Joseph Design Build, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $108K - $108K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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