Staff Engineer AI Agents

About Zuma

Zuma is pioneering the future of agentic AI in property management. We build AI agents that act as property managers, handling the full spectrum of interactions with both prospects and current residents on behalf of our clients. Our agents don’t just assist human workflows; they own them end-to-end, operating across leasing, collections and resident communications. Zuma has ambitions to continue expanding into adjacent work activities in tangential areas of property management.

Our flagship product is a multichannel leasing agent, a fully autonomous AI that engages prospects over voice, text, and email, guiding them through the entire leasing journey without human intervention. Today we sell exclusively to large enterprise property managers and are trusted by some of the largest apartment operators in the country, including Blackstone’s residential portfolio, the largest apartment management group in the US. This is a ~$200B market that has been dramatically underserved by technology, and we’re rewriting the playbook.

Off the back of our Series A, Zuma is scaling rapidly. We’ve raised over $17M to date with backing from world-renowned investors including Andreessen Horowitz (a16z), Y Combinator, King River, Range Ventures, and distinguished angels like YC’s former COO, Qasar Younis.

Role at a Glance

This is a rare chance to shape the future of how an entire industry operates — not in theory, but in production, at scale, touching real customers and physical assets every day. At Zuma, human and AI agents work side by side, and you'll help define what that collaboration looks like at its best.

Help define how humans collaborate with intelligent systems in one of the largest and most underserved industries in the world: property management.

• Shape the future of property management by designing the human + AI workflows that will define the industry standard for the next decade.

• Join an A+ team with a wide range of experience across AI, enterprise SaaS, and proptech — people who have built and scaled products before and are doing it again.

• Own and influence how Zuma approaches entirely new workflows as we expand beyond leasing into the full resident lifecycle.

• Learn and establish best practices for deploying agents in real business operations — systems that touch real customers, real money, and real physical assets. This is the hardest and most valuable kind of AI engineering there is.

• Architect, build, and deploy production AI agents using modern agent frameworks (LangGraph, CrewAI, AutoGen, or equivalent), owning the full lifecycle from design to reliability in production.

• Define the technical patterns and standards for how agents are built across the engineering org — you will be setting the playbook others follow.

• Lead the rebuilding of core platform systems — including our onboarding/configuration system, integration framework, and AI performance analytics infrastructure.

• Collaborate directly with the VPE and product leadership to translate product vision into agent architecture, and make high-stakes technical trade-offs with confidence.

• Own agent reliability, observability, and continuous improvement — defining how we measure, monitor, and iterate on agent behavior in production.

• Work across the stack (backend services, LLM orchestration, integrations, data pipelines) to ship agents that are robust and scalable.

• Tame legacy code and lay down new foundations — patterns and architecture you create will be inherited by the engineers who come after you.

• Be a close partner to the product and operations teams, turning their domain needs into intelligent automated workflows without requiring domain expertise upfront.

• 5+ years of software engineering experience with a strong backend or distributed systems foundation.

• Demonstrated experience designing and shipping AI agents in production — not just prototypes. You've owned agent systems that real users depend on.

• Hands-on experience with at least one modern agent framework such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or a comparable orchestration layer.

• Deep familiarity with LLM integration patterns — prompt engineering, tool/function calling, memory systems, retrieval-augmented generation (RAG), and agent evaluation.

• Experience building reliable, observable agentic systems: tracing, error handling, fallback strategies, human-in-the-loop checkpoints, and graceful degradation.

• Strong proficiency in Python and/or TypeScript — the languages our agents live in.

• • Ability to work across ambiguity and drive projects from problem definition through production deployment independently.

• Clear, direct communicator who can translate complex technical architectures for non-technical stakeholders.

• Experience with multi-agent systems — coordination patterns, agent-to-agent delegation, and conflict resolution.

• Familiarity with vector databases and embedding strategies (Pinecone, Weaviate, pgvector, etc.).

• Prior experience at a startup or high-growth company; comfort shipping fast and iterating in production.

• Background in building self-serve platform or integration infrastructure.

• Experience with workflow automation platforms or business process orchestration.

• Experience with telephony integrations (Twilio or similar) and building voice-capable agents or chatbots across text and voice channels.

• Familiarity with speech-to-text, text-to-speech, or real-time audio streaming pipelines in production AI systems.

• Classical ML experience — supervised/unsupervised learning, feature engineering, model training and evaluation outside of LLM contexts.

• Python, TypeScript/Node.js 

• OpenAI, Anthropic

• LangGraph, OpenAI Agents SDK, custom orchestration layers 

• AWS, AWS ECS, PostgreSQL, Redis

• RealPage, Entrata, Yardi, and other property management systems

• Agents are the core product, not a side feature. Every line of agent code you write ships to hundreds of thousands of rental units.

• Series A company with a16z and YC backing — enough resources to build, early enough to matter.

• Remote-friendly culture with a bias toward outcomes, not facetime.

• Competitive compensation package including meaningful early-stage equity.