Senior Software Engineer | Remote

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 owning systems end to end, from code through cloud deployment and DevOps.

 

Strong candidates may also have:

• Hands-on experience with any of: React/Next.js, FastAPI/Django/Flask, Docker/Kubernetes, PostgreSQL/MongoDB/Redis, Temporal/Airflow/Dagster, Kafka/RabbitMQ, or AuthN/AuthZ patterns.
• Have experience with FinOps at scale — cost attribution, spend optimization, and keeping cloud bills predictable as media and compute workloads grow.
• Experience shipping agent systems — MCPs, sandboxing, AI agent infrastructure.

 

How we're different

Most AI tools never leave the demo. Ours can't hide — they're used by expert teams who notice every miss, against a real P&L, all day, every day. We're a small, senior group of scientists and veteran engineers from leading tech and consumer companies, and we hire deliberately for people who want hard problems, real users from day one, and room to own things end to end.

We work in tight loops: an idea can go from a prototype to a product in front of operators and clients in days. We start with unique leverage — proprietary data and the industry-leading playbook behind billions in ad spend — build the tools in-house, prove them in production at massive scale, and then ship the winners beyond TubeScience. Velocity beats perfection until something earns the right to scale.

Come work with us!

TubeScience Labs is the applied-AI team inside TubeScience, headquartered in Los Angeles. You'll join a small, senior group with an outsized surface area: real autonomy, a one-of-a-kind dataset, and a direct line from your code to real customers.

We offer competitive compensation, comprehensive benefits, plus generous access to frontier and open-weight models for day-to-day work, prototyping, and evals. Most of all, we offer the rare thing in applied AI: hard problems, expert users from day one, and the room to take an idea from prototype to shipped product. Come build at the frontier with us!

We encourage you to apply even if you don't believe you meet every single qualification. Not all strong candidates will check every box as listed, and the best people we've hired rarely did. If this work excites you and you think you could do it well, we'd rather hear from you than have you rule yourself out — so please submit an application.