Job description

What is Bilt? Bilt is the membership for where you live. We started by rewarding people on their biggest expense, rent, and have grown into a network connecting members, property managers, and local merchants across dining, travel, fitness, and everyday spending. The role Concierge runs in the Bilt app, on the web, and over SMS, iMessage, and email. Members use it for dining, rent, payments, travel, and support. Restaurants and property managers use it to talk with their customers, and their staff use it as an assistant. It handles tens of thousands of conversations a day, with 2–3x spikes at the start of every month, and you'll build and run the backend behind all of it. What you'll work on • Agent capabilities end to end: tools, workflows, prompts, and the integrations with Bilt and partner systems behind them • The agent runtime: multi-agent orchestration, context and memory, prompt caching, and routing across frontier and open-weight models • Performance: p50 and p99 across the full request path, bursty traffic, and cost per conversation • Eval harnesses, LLM judges, and conversation replay, so every model and prompt change is measured before it ships • Assistants for merchants' staff that work from the business's own data, and browser agents that can act where no API exists • Proactive features that drive engagement with members and merchants' customers: briefings, follow-ups, and outreach that land at the right moment • The conversation platform: one inbox where the agent, merchant staff, and Bilt support share threads across SMS, iMessage, email, web, and in-app chat • Messaging: realtime streaming replies, attachments, message ordering and idempotent delivery, carrier and iMessage sending limits, and handing off to a person at the right moment • Authentication and fine-grained authorization, so the agent only sees and does what it should for members, merchants, and staff How we build We are an AI-native team. Agents are part of how we write, review, debug, and operate everything. • Writing. Coding agents like Claude Code and Devin write much of our code. You design it, review it, and own what ships • Reviewing. Every PR gets AI review against rules we write down (prompt-cache pitfalls, security, performance) before a human codeowner signs off • Debugging. Agents have access to our production tooling through an internal MCP gateway: logs, traces, analytics, feature flags, and the service catalog. Investigations usually start with an agent pulling the evidence • Sharing context. Repos carry agent instructions, and we keep a shared library of skills and playbooks, so recurring work becomes a single command and every agent starts with the team's context • Owning. Each engineer owns a large surface from design through rollout and on-call. We ship daily behind flags, treat performance and availability as acceptance criteria, and read real conversations, not only dashboards You have • 4+ years building and operating backend services in production • Strength in at least one backend language and experience running services in the cloud. We mostly use Python (FastAPI), Java (Quarkus), and Go on Google Cloud • Solid distributed systems fundamentals (async processing, pub/sub, idempotency, retries, caching), and you debug across services with logs, traces, and metrics • Hands-on experience with LLMs, at work or on your own, and curiosity about making them reliable • Product judgment: you can read a bad conversation and tell whether the cause is the prompt, a tool, the data, or a service • Daily use of AI coding agents, and good judgment about when to trust their output • Clear writing. Design docs, PR descriptions, and the prompts you give agents all matter here Bonus • Shipped an LLM-powered feature to real users, and can explain what broke and how you measured it • Agent frameworks (Google ADK, LangGraph, or similar), tool calling, evals, LLM-as-judge • Model serving and inference: provider routing, open-weight models, latency and cost tuning • Messaging or realtime infrastructure • Browser automation or computer-use agents • Identity and access: OAuth/OIDC, token exchange, fine-grained authorization Benefits • Compensation - We offer a competitive salary with a meaningful stake in the company via equity and our performance bonus program • Health insurance for you (& your loved ones) from day one - Enjoy a One Medical Membership, wellness stipends, family programs and more, on us. We’ve got you and your family covered from day one. • 401k plan with a match - Retirement may feel more like a pipe dream than a reality but we’re here to help you get there. • Commuter FSAs - We believe the best ideas come from being together in one place. We just don’t think getting there should be so expensive. • UNLIMITED PTO - Because we believe that working hard shouldn’t mean always working. Take time for yourself as often as you need it. • Exclusive Employee only Bilt Points - We give our employees unique opportunities to earn points throughout their time at Bilt. • Team Events - We believe in human connection so we hold events to help our employees break from the monotony of the typical work week. At Bilt Rewards, we believe in transparency and we do our best to make sure the company and our candidates are on the same page as it relates to compensation. In addition to posting salary ranges for our open roles, candidates should expect to be asked about compensation expectations and requirements early on in their interview process. Our goal is to highlight when expectations and Bilt’s salary range may be out of sync, and work with the candidate to determine whether it makes sense to continue conversations. We are considering candidates with differing levels of expertise for this position. Leveling will be based upon your experience and performance in the interview process. Where a new hire falls within a range will be based on their skills and experience, and how these competencies compare across other employees in the same role. Bilt's bands are designed to allow for individual compensation growth within the role. As such, new hires typically start at the lower end of the range. Bilt Rewards performance and outcomes - should you join the company, you will have the opportunity to grow your salary over time. The salary range for a Back End / AI Engineer is $170,000 - $300,000 (based on YOE/level) , and you will be eligible for equity and an annual performance-based bonus.

Requirements

Must-haves

What is Bilt?
What you'll work on
Agent capabilities end to end: tools, workflows, prompts, and the integrations with Bilt and partner systems behind them
The agent runtime: multi-agent orchestration, context and memory, prompt caching, and routing across frontier and open-weight models
Performance: p50 and p99 across the full request path, bursty traffic, and cost per conversation
Eval harnesses, LLM judges, and conversation replay, so every model and prompt change is measured before it ships
Assistants for merchants' staff that work from the business's own data, and browser agents that can act where no API exists
Proactive features that drive engagement with members and merchants' customers: briefings, follow-ups, and outreach that land at the right moment
The conversation platform: one inbox where the agent, merchant staff, and Bilt support share threads across SMS, iMessage, email, web, and in-app chat
Messaging: realtime streaming replies, attachments, message ordering and idempotent delivery, carrier and iMessage sending limits, and handing off to a person at the right moment
Authentication and fine-grained authorization, so the agent only sees and does what it should for members, merchants, and staff
How we build
We are an AI-native team. Agents are part of how we write, review, debug, and operate everything.
Writing. Coding agents like Claude Code and Devin write much of our code. You design it, review it, and own what ships
Reviewing. Every PR gets AI review against rules we write down (prompt-cache pitfalls, security, performance) before a human codeowner signs off
Debugging. Agents have access to our production tooling through an internal MCP gateway: logs, traces, analytics, feature flags, and the service catalog. Investigations usually start with an agent pulling the evidence
Sharing context. Repos carry agent instructions, and we keep a shared library of skills and playbooks, so recurring work becomes a single command and every agent starts with the team's context
Owning. Each engineer owns a large surface from design through rollout and on-call. We ship daily behind flags, treat performance and availability as acceptance criteria, and read real conversations, not only dashboards

Nice-to-haves

Shipped an LLM-powered feature to real users, and can explain what broke and how you measured it
Agent frameworks (Google ADK, LangGraph, or similar), tool calling, evals, LLM-as-judge
Model serving and inference: provider routing, open-weight models, latency and cost tuning
Messaging or realtime infrastructure
Browser automation or computer-use agents
Identity and access: OAuth/OIDC, token exchange, fine-grained authorization

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

llmapiaipythonjavago

Platform: gem
Effort: medium

Benefits

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Health Insurance

401k

Pto

Wellness