ABOUT RENTSPREE

RentSpree is the nation’s leading home rental software. Our award-winning product suite simplifies the entire rental journey—from applications and screening to payments and lease management. Our mission is to bring transparency, efficiency, and trust to agents, landlords, and renters alike.
With over 3 million users, partnerships with 300+ leading real estate companies and MLS platforms, and nine years of consistent growth, RentSpree is at the forefront of proptech innovation. Our award-winning solutions deliver automation and real-time insights that make renting faster, easier, and more secure for our customers.
RentSpree is a proud Forbes’ Best Startup Employer and we were also ranked one of BuiltIn Seattle’s Best Places to Work. As one of the fastest-growing property tech startups in the U.S., we’re looking for talented problem-solvers to contribute meaningful impact as we scale.

SKILLS YOU BRING

• Bachelor’s or Master’s degree in computer science, data science, machine learning, or a related quantitative field.
• 6+ years in applied ML, NLP, or AI engineering, with demonstrated experience in shipping production AI/ML features.
• Expert proficiency in Python modern ML/LLM frameworks (PyTorch, Hugging Face, LangChain, LlamaIndex), and strong SQL/data pipeline skills.
• Proven hands-on experience fine-tuning LLMs, building RAG pipelines, and creating agentic AI workflows (e.g., multi-step reasoning, autonomous agents, internal AI copilots).
• Experience defining and running LLM evaluation frameworks and integrating human feedback loops.
• Strong communication skills to bridge technical and non-technical teams, with the ability to influence AI product direction.
• SaaS or proptech domain experience is a plus, especially with real estate transaction, tenant screening, or property management data.

WHAT YOU’LL DO

• Strategic AI Alignment: Collaborate with Product, Engineering, and Data leaders to define and refine RentSpree’s ML/LLM strategy, ensuring alignment with business priorities, roadmap milestones, and measurable success criteria.
• AI Use Case Translation: Partner with analytics teams to turn business challenges into well-defined, testable LLM/ML use cases, accelerating time-to-production for AI solutions.
• Mentorship & Skill Uplift: Coach data scientists, analysts, and engineers in LLM concepts, evaluation design, RAG patterns, and applied AI/ML best practices.
• Full AI Lifecycle Ownership: Design, fine-tune, evaluate, and deploy ML/LLM models for both product-facing features and internal AI assistants, with a focus on scalability, safety, and maintainability.
• Agentic AI Infrastructure: Architect and implement workflows for agentic AI systems — including multi-agent coordination, tool integration, memory management, and retrieval-augmented generation (RAG) — enabling autonomous and semi-autonomous AI-powered decisioning.
  Our job descriptions evolve with our business needs and priorities. In addition to the description above, your role may include additional tasks, projects and team support as needed.