Build the engine behind the outcome:
Most applied AI companies sell tools. throxy sells the work itself: pipeline that closes revenue. Our customers don't care how it happens. They care that it does. That distinction changes everything about the engineering. Every improvement we ship makes the service faster and harder to compete with. Better models don't threaten us. They compound us.
We have product-market fit, month-over-month growth, and a clear roadmap. Small, senior engineering team where your code directly drives revenue. This is a rare window: early enough to build foundational systems, our customers range from Fortune 500s to the fastest-growing startups.
Own problems end-to-end. You'll build the systems you've seen at other companies, from scratch, with real users from day one. Scope it, build it, ship it, iterate.
Why throxy:
The model compounds you
Every AI improvement makes our service faster and harder to compete with. You're building on a tailwind, not racing against one.
Proven, not speculative
Product-market fit, month-over-month growth, and customers paying for outcomes. The business works. Now we need to scale the engineering.
Build what you've only seen
Dialer infrastructure, agent systems, intelligence pipelines. The systems you've watched other companies build, you'll own here.
Small team, real leverage
A handful of AI-augmented engineers shipping systems that generate pipeline for dozens of customers, Fortune 500s among them. Your work has outsized impact because the team is small and the surface area is large.
The stack:
We're building with TypeScript, React, Next.js, and Bun. Our infrastructure runs on AWS: Postgres, ClickHouse, OpenSearch, and Datadog, with an Elixir core under the dialer. We care about clean code, fast iteration, and engineering excellence.
These are not roadmap items. They are in flight:
Real-time calling infrastructure on an Elixir core, placing millions of calls a month, with inbound, callbacks, and voicemail detection on local models.
Provider waterfalls, LLM qualification and evaluation pipelines, a self-healing lead dataset, and scoring that decides who gets called next.
Post-call insights and call-outcome classification, fed back into the next campaign.
Every tool we've outgrown gets rebuilt as our own.
Strong signals:
Experience at a high-calibre tech company: FAANG, Bloomberg, Stripe, Shopify, Revolut, Datadog, or similar.
Time at a startup or small team (< 20 people) where you wore many hats.
Experience building with AI/LLMs: AI agents, RAG systems, or LLM-powered features.
You've built something end-to-end on your own: a side project, an open source tool, a past business.
What we're looking for:
Our hiring process:
We get to know each other. We'll talk about your experience, what you're looking for, and give you a clear picture of throxy.
A hands-on assessment of your coding and problem-solving skills.
Meet more of the team, talk about culture, role expectations, and comp.
We aim to move through the full process in 72 hours.