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About This Role
About Promobile
Promobile is an experiential marketing agency that designs and produces large\-scale brand activations for major consumer brands. To scale what we do, we're building an AI\-powered software platform that plans, prices, and operates those activations — used first by our own team, then offered to the wider experiential marketing industry.
You'd join the small software team behind that build, backed by an operating agency with real customers, real budgets, and deep domain expertise. We ship production AI systems, not demos.
What We're Looking For
You've shipped at least one production system that calls an LLM and reasons about the output structurally. Not chat completions or one\-shot prompts, but a real pipeline with prompt caching, Zod\-validated structured outputs, evaluation harnesses, and the ability to debug why a model returned the wrong shape three weeks after you shipped it.
On top of that, you've shipped full\-stack TypeScript products in production. You've debugged a broken RLS policy. You know when to reach for a database function vs. application logic, when something should be configurable vs. static state. You have opinions about data fetching patterns and can explain why optimistic updates matter for UX.
You're comfortable being the most senior technical voice in the room. You'll push back on bad ideas, but you'll also execute on decisions even if it means adding complexity on the backend so the user gets the best experience.
Experience: 6\+ years building production web applications, with at least 1 year of that on production AI/LLM systems.
Not The Right Fit If
- You've used LLM APIs but haven't designed a production pipeline around them
- You're transitioning from frontend\-only or backend\-only work and want to "grow into" full\-stack
- You need an engineering manager, a senior peer to learn from, or structured onboarding to ramp up
- This would be your first Senior, Lead, or Principal role
Responsibilities
- Own delivery of a modern SaaS web application with AI integrations
- Architect modern web features deployed to serverless infrastructure and cloud (edge) functions for long\-running tasks. Prioritize correctness, performance, and flexibility
- Model complex problems in a relational database, manage schema migrations, optimize query performance, and secure data with RLS policies
- Write production\-quality TypeScript across the full stack: Next.js App Router (React) with TailwindCSS on the front end, Supabase Edge Functions (Deno) on the back end, and PostgreSQL with RLS for data integrity
- Build and maintain the AI pipeline, an extensible orchestration system (Claude Haiku \+ Sonnet) that generates structured outputs with prompt caching and Zod\-validated schemas
- Implement and iterate on UI features using our Glow UI design system, making sure every interaction feels fast and intentional
- Manage CI/CD pipeline via Github Actions and Vercel webhooks
- Participate in and challenge product and UX discussions. We operate with full transparency, so you're expected to have opinions on what to build and why
- Develop test suites that provide important feedback in a fast\-moving startup environment. It's important to move fast, but also move forward, and a strong test suite prevents regressions
Culture
We're a small team that punches far above our weight. A few things define how we work:
We have strong architectural opinions. Enforcing strong architecture allows us to move fast without reinventing the wheel on every feature. Pick a direction and stick with it until it needs upgrading.
We move fast without cutting corners. Fast means clean builds, clean lint, and working tests, not skipping them. Surprises cost time, and minimizing them upfront allows us to move fast.
We don't hide from bad news. If something is broken, architecturally wrong, or likely to cause problems at scale, we address it head on and stay open to change. Prioritize the user always.
We treat AI as a collaborator. We use Claude heavily in our development workflow (Claude Code, Cowork, and in\-product pipelines). You should be comfortable prompting, evaluating, and iterating on AI\-generated code rather than rubber\-stamping it.
Technology
- Next.js 16 (App Router)
- React 19 \+ Tailwind CSS v4
- TypeScript
- Supabase (PostgreSQL)
- Supabase Auth (JWT)
- Deno (Supabase Functions)
- Anthropic Claude API (Haiku \+ Sonnet)
- SWR \+ SWR Mutation
- Vitest
Logistics
- W\-2 (US)
- NYC\-based: in\-office at our Brooklyn office
Pay: $150,000\.00 \- $210,000\.00 per year
Application Question(s):
- Describe the most complex production system you've designed and shipped end\-to\-end. What was the hardest architectural decision you made, and what would you do differently now?
- A link to something you've built (a GitHub repo, deployed product, technical writeup, or portfolio piece). Add one sentence on what we should look at and why.
Work Location: In person
Salary Context
This $150K-$210K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 219 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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At ReClick, 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 $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 22% below the category median. Disclosed range: $150K to $210K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
ReClick AI Hiring
ReClick has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Brooklyn, NY, US. Compensation range: $210K - $210K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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