Job Opportunities API

About TubeScience Labs TubeScience Labs is the applied-AI team inside TubeScience — the largest performance-video company in paid social. TubeScience is Meta's largest creative partner and AppLovin's #1 creative partner, producing 8,000+ original ads every month from a 100,000 sq ft Los Angeles studio, backed by a library of 1.6 million+ performance ads and $2B in annual managed ad spend. That makes one of the richest first-party creative-performance datasets anywhere. Labs turns that data — and the playbook behind billions in spend — into frontier AI tools that actually ship. Our products run in production against real creative, real deadlines, and real budgets every day. The tools that graduate internally become products we ship to external clients.   About the Role TubeScience Labs is hiring a Full Stack Engineer to join our core platform team and serve as a technical lead across the full stack — the frontend tooling for complex ad workflows, the cloud infrastructure, and the backend systems that tie our AI integrations together. You'll own technical direction across these surfaces and shape how the platform evolves as the product and the AI systems underneath it grow in tandem. This is a deeply hands-on role. You'll write code, set architectural direction, and make the hardest technical calls — but you won't be executing someone else's roadmap. You'll be one of a small number of engineers in the room when architecture gets decided, and your judgment will carry weight from day one. You'll help us figure out not just how to build, but what to build. We care as much about craft as we do about capability. The right person obsesses over how systems behave — performance, reliability, the details that separate a tool people tolerate from one they trust — and knows how to put the systems in place so a fast-moving team can keep that bar high. This role is fully remote.   In this role, you will: Build the operator tooling : Take ownership of entire subsystems of the applications we are building for ad operations at real scale and complexity — not a marketing site, but a production tool operators rely on daily to run large campaigns. The hard problems here are state, performance, and making complex workflows feel simple. Tie the AI together : Build the backend services and APIs that power multiple production AI agent integrations, and make them reliable, observable, and fast under real load. Own the infrastructure : Build and operate cloud infrastructure on AWS/GCP/Azure — deployment pipelines, monitoring, autoscaling, the foundation everything else runs on. Work across the entire stack : Push into the layers most engineers don't — media pipelines, durable workflows, distributed systems internals — to unlock things that aren't possible at the app layer alone. Shape the architecture : Bring an engineering point of view to how the platform grows — what we build, in what order, and why.   You might be a good fit if you: Have built large-scale SaaS platforms from scratch — making the foundational decisions and living with them — not just joined a mature codebase, and can point to systems you owned end to end. Are strong in JavaScript (React, Vue, or Angular) and can articulate why you reach for the one you reach for, and strong in Python — you've shipped and operated production services, not just scripts. Have real depth below the abstraction layer — at least one low-level language (C, C++, Rust, Zig, or equivalent) — and it informs how you build above it. Know media pipelines firsthand: transcoding, ffmpeg-class tooling, media transformation, cloud storage. This sits at the core of what we build. Design for observability from the start — tracing, metrics, structured logging — and have built durable, fault-tolerant workflows that hold up in production. Care about testing that catches real problems, and are comfortable owni