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About This Role
Luxury Presence is building the AI growth platform for real estate. Backed by Bessemer Venture Partners and other top investors, we're a Series C company that has hit $100M in annual recurring revenue. More than 90,000 real estate professionals, including over 30% of the WSJ Real Trends top 100 agents in the United States, use us to run and grow their business.
The Opportunity
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AI Social is one of the most ambitious products we've ever shipped. An AI agent that produces on\-brand Instagram carousels and reels for every Luxury Presence customer, from raw input data to finished media. It isn't LLMs writing code. It's LLMs designing, choosing the photo, voicing the script, picking the cuts, and composing the final frame.
As the Generative Media Engineer on AI Social, you own the render pipeline, the prompts and agent loops that drive it, and the visual quality of what ships. Product and content strategy partners set the direction; engineering and design peers ship alongside you. This is an unusual role at the intersection of code, design, AI, and social media. You don't fit the normal engineering mold, that's the point.
Skills and experience
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- Solid working knowledge of TypeScript and Node.js. You're comfortable shipping production code and you're ready to grow into our stack.
- Hands\-on experience with LLM\-driven systems. Prompts, structured outputs, tool\-calling, agent loops, evals.
- At least one shipped piece of generative media in image, video, voice, or music that someone other than you has seen and used.
- A portfolio or body of work that demonstrates both technical depth and design sensibility.
- Bonus: a personal creator practice somewhere. A feed, a portfolio, a side project, a YouTube channel, an Are.na. Public, ongoing, under your own name. You understand the medium because you've lived in it.
- Formal or self\-taught grounding in design, visual art, film, or motion.
- Comfort moving between exploratory PoC work and shipped\-to\-customers polish, with judgment about which mode is appropriate when.
Technical fluency with AI tools
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- You're already using Claude Code, Cursor, or similar AI\-assisted development tools in your daily workflow. We're a Claude Code shop and we expect you to be fluent.
- Familiarity with MCPs and how tools like Figma, Notion, and Linear connect into AI\-assisted workflows. You understand how these tools and integrations fit together and can help the team get more out of them.
- Beyond fluency, you're a dabbler and experimenter. You try new prompts, push their limits, and share what you learn. You have opinions about where the medium is headed.
Mindset
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- You're a tinkerer. You don't just build what's asked; you spot leverage points and build what's needed.
- You care about systems more than features. You'd rather build a template family that ships ten posts well than ten bespoke posts that age out by next week.
- You build on shared systems. Constraints feel like leverage, not obstacles.
- You have strong convictions about quality, but you ship. You know the difference between polish that matters and perfection that stalls.
- You partner well with non\-engineers. You'll collaborate across product, design, and content strategy. Patience and clarity with people outside engineering is non\-negotiable.
- You operate with high ownership and low ego. You take direction from product and strategy partners on what to build, and you raise the bar on everything you ship.
What You Won't Be Doing
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To be clear about what this role is not:
- You won't own platform architecture or distributed systems work. You'll partner with engineering teams that do, while you stay focused on the AI Social pipeline itself.
- You won't own the social strategy. You'll have a real voice in shaping it, but the call sits with our product and content strategy partners.
- You won't be managing people on day one. This is a hands\-on IC role with the expectation that you lead through craft and influence.
Tech stack
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- Platform: Node, TypeScript, React, Postgres, AWS, Temporal
- LLMs and agents: the major model SDKs, with tool\-calling, structured outputs, and multi\-step agent loops
- Media pipeline: image and video generation (Veo for video), voice and music synthesis (ElevenLabs), composite (Shotstack), and headless browser rendering
Join us in shaping the future of real estate
The real estate industry is in the midst of a seismic shift, and the future belongs to those who break new ground. As one of the fastest\-growing companies in the proptech and marketing sectors, Luxury Presence challenges the status quo of what technology can do for real estate agents, leaders, and brokerages.
We're a team of agile and tenacious innovators working collaboratively to drive the industry forward. Together, we build game\-changing products that empower modern real estate entrepreneurs to dominate their markets. From award\-winning web design to agile SEO solutions to cutting\-edge AI tools, we deliver tech that anticipates market shifts and keeps our clients ahead of their competition.
Founded in 2016 by Stanford Business School alum Malte Kramer, Luxury Presence has grown to a global team ranked on the Inc. 5000 fastest\-growing companies list three years in a row. We're backed by world\-class investors, including Bessemer Venture Partners, NextEquity Partners, Toba Capital, and Switch Ventures, and have raised $89 million to date.
More than 18,000 real estate businesses rely on our platform, including 30% of the Wall Street Journal RealTrends top agents and teams. Additionally, many of the industry's most powerful brokerages rely on Luxury Presence as a trusted business partner.
Every year since 2020, Luxury Presence has ranked on BuiltIn's Best Place to Work lists. HousingWire named our founder and CEO a 2024 Tech Trendsetter, we've received several Tech100 Awards, and we just scored an Inman Innovation Award for Best AI\-Powered Platform.
Luxury Presence is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Luxury Presence, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Luxury Presence AI Hiring
Luxury Presence has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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