Senior Full Stack Engineer

Company Description

100XLT.ai empowers local publishers by providing seamless solutions to capture a greater share of the $47 billion in local ad spend that often goes to major platforms like Google and Meta. We offer a hassle-free way for publishers to create advertising campaigns that support local businesses while boosting their own revenue. Our platform enables local businesses to have a stronger community presence through publishers’ networks, creating value for both parties. With growing adoption among major publishers and regional media operators, 100XLT.ai is revolutionizing local advertising. At 100XLT, our success is inherently tied to the success of our partners.

About the Role

We build AI-powered content products, and our engineering team ships through orchestrated AI agents rather than hand-typed code. We're looking for an engineer who treats coding agents as a force multiplier — someone who builds agents, directs a fleet of them, verifies their work, and ships real features at a pace traditional teams can't match.

This role is less about how fast you type and more about how well you think, decompose, and verify. You burn tokens to deliver impact, and you know exactly when it's worth it.

The one-line test: Can you take a vague feature request, spec it, orchestrate agents to build it, verify it's correct, and ship it before a traditional team finishes grooming the ticket? If yes, apply.

This is an on-site position — we move fast, pair tightly, and think best in the same room.

A note on experience: the years-of-experience bar below is a guideline, not a gate. If you have genuine AI-first build experience — you've built agents, harnesses, or automations that made you or your team dramatically faster — we want to talk, even if you're earlier in your career. Demonstrated agentic output beats a long résumé.

What you'll actually do

• Own features end-to-end across a modern full-stack TypeScript codebase — from a vague request to production.
• Decompose ambiguous problems into specs an agent can execute, then orchestrate, review, and tighten the loop.
• Build agents, workflows, and automations that push the whole team's productivity toward 100X.
• Work on the AI core of the product: LLM-driven generation, multi-model routing, and data pipelines that turn raw inputs into polished output.
• Hold a high bar — correctness, security, and maintainability — while moving fast. Speed without judgment isn't what we're after.

Must-haves

Behavioral

• Builds agents to multiply the team's output. You don't just use coding agents — you build them: custom agents, workflows, harnesses, and automations that take a team from 1X to 100X. When a task is repetitive or parallelizable, your instinct is to make an agent do it.
• Output measured in shipped impact, not effort. You ship features, not lines.
• Lives in agentic tooling. Daily, fluent use of Claude Code / Cursor / equivalent — with real opinions on getting more out of them.
• Verifies relentlessly. You read what the agent produced, run it, and own the result. No blind merges.
• Decomposes well. Turns fuzzy asks into clear, executable specs — this is the core skill.
• Spends tokens like an investment. Comfortable fanning out agents aggressively, and able to judge when it pays off.
• High agency. Unblocks yourself, fills gaps, and pushes things to done without hand-holding.

Technical

• Senior-level command of TypeScript across both server and client (typically 5+ years building production software — but strong AI-first builders with less time are encouraged to apply).
• Strong with a modern Node backend framework and a modern React frontend — deep enough to debug what an agent gets 90% right.
• Solid relational DB / ORM fundamentals and REST/API contract design.
• Comfortable with cloud deployment (auth, storage, serverless/managed compute) and reading systems you didn't write.
• Has shipped at least one LLM-powered feature end-to-end — prompting, model tradeoffs, cost/latency awareness.
• Tests, debugging, and security instincts that hold up under speed.

Good-to-haves

• Real-time / collaborative editing (CRDTs) experience.
• Worked in a monorepo with shared packages and type-safe codegen between back and front end.
• Experience with scraping / discovery / RAG-style data pipelines.
• Prompt engineering and multi-model routing at scale.
• Built internal tooling that made a team measurably faster.
• Contributions to open-source agent tooling or MCP integrations.

How we work

Ship-daily culture, tight feedback loops, and AI-orchestrated review. We give you the most capable models, generous token budgets, and the autonomy to use them. Bring the judgment.