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About This Role
New York Engineering In office Full-time What is Bilt? Bilt is the membership for where you live.
What started as a way to reward people on their largest expense has turned into an ecosystem connecting our members, property managers and local merchants. Travel, dining, fitness, everyday spend. We are building systems across payments, credit, loyalty, and commerce all at once.
We run lean and move quickly. Every person here has a real impact on what gets built and how fast it happens.
This role comes with real ownership and high expectations. If you prefer clearly defined lanes and structure, this will feel uncomfortable. If you want to build quickly, have a real say in what gets built, and work on products used by millions, you will likely do your best work here. 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.
Req ID: R349
Salary Context
This $170K-$300K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $192K across 198 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Bilt Rewards, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($235K) sits 7% above the category median. Disclosed range: $170K to $300K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Bilt Rewards AI Hiring
Bilt Rewards has 1 open AI role right now. They're hiring across AI Software Engineer. Based in New York, NY, US. Compensation range: $300K - $300K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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