Interested in this AI/ML Engineer role at Luxury Presence?
Apply Now →Skills & Technologies
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 on track to hit $100M in annual recurring revenue in the next six months. More than 87,000 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
-------------------
We don't just use AI. We build with it, ship with it, and think with it. We're in the top 1% of companies applying AI effectively, not just to our products, but to how we build them. With an unlimited budget for Anthropic tokens, our engineers use AI agents to write, review, and ship production code every day. We're building toward a world where humans design systems and AI builds them, and we're already further along that path than almost anyone else.
As a Staff Software Engineer, you'll be a technical leader at the center of this transformation. You'll shape platform architecture, drive AI\-powered product delivery, and raise the bar for what a small, AI\-augmented engineering team can accomplish. This is a role for someone who already lives in their terminal with AI, who has strong opinions about how AI changes system design, and who wants to help define what a next\-generation engineering organization looks like.
What You'll Do as a Staff Engineer on Our Auggie Platform Team
------------------------------------------------------------------
Here are examples of the kinds of initiatives you'd work on today:
Multi\-agent orchestration and execution. Design and build the core systems that let multiple autonomous agents collaborate, delegate, and execute complex business workflows in production — not chatbot interactions, but structured multi\-step operations running across real business data.
Autonomous workflow automation. Build the automation layer that turns business processes into agent\-driven workflows with structured data models, conditional logic, and human\-in\-the\-loop review queues that keep autonomous systems safe and reliable.
Auto\-generated application UIs. Architect the systems that dynamically generate functional user interfaces from agent outputs and structured data models, letting non\-technical users interact with AI\-powered applications without writing code.
Platform integration and MCP infrastructure. Own the integration layer connecting Auggie to internal and external systems — building MCP servers, managing agent\-to\-system communication, and enabling the platform to operate across tools like Slack, Notion, Salesforce, and custom data sources.
What We're Looking For
--------------------------
### Attributes
- You already build with AI daily. You use Claude Code as a core part of your workflow, not as a novelty
- You have strong opinions, loosely held, about how AI changes software architecture, team structure, and engineering culture
- You think in systems. You connect technical decisions to customer outcomes and long\-term business value
- You communicate clearly and directly. You can explain complex tradeoffs to product, design, and executive stakeholders
- You're energized by ambiguity and speed. You thrive in a fast\-growing company where the roadmap evolves and ownership is real
- You like to have fun at work. We take our craft seriously, but we don't take ourselves too seriously. We celebrate wins, crack jokes, and genuinely enjoy building together
### Skills and Experience
- 8\+ years of professional software engineering experience, with meaningful time in senior or staff\-level roles
- Production experience building multi\-agent or agentic AI systems — not prototypes or demos, but systems running against real data with real users
- Deep familiarity with LLM integration patterns: prompt engineering, tool use / function calling, RAG pipelines, and structured output parsing
- Experience with agent orchestration frameworks, agent\-to\-agent communication, or autonomous workflow execution
- Strong backend engineering fundamentals: TypeScript/Node.js, event\-driven architectures, and distributed systems
- Track record of owning product\-oriented technical initiatives end\-to\-end, from system design through production
- Experience with MCP servers, LLM\-based validation, or spec\-driven development workflows is a strong plus
- Startup or high\-growth experience preferred, but big\-company background is fine if the AI systems experience is genuinely strong
### Tech Stack
- Backend: Node/TypeScript, Python
- AI: Anthropic Claude, MCP, LangGraph
- Data: Postgres, Redis, SQS
- Infrastructure: AWS, Kubernetes, Temporal
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. 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.
Salary Context
This $200K-$270K range is above the 75th percentile 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 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 $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 ($235K) sits 41% above the category median. Disclosed range: $200K to $270K.
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.
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 - $270K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.