Note: The job is a remote job and is open to candidates in USA. TubeScience Labs is the applied-AI team inside TubeScience, the largest performance-video company in paid social. They are seeking a Senior Software Engineer to join their core platform team, where the candidate will serve as a technical lead across the full stack, owning technical direction and shaping the platform's evolution while building tools that operate at scale for ad operations.
Responsibilities
- 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
Skills
- 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
- 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
Benefits
- Competitive compensation
- Comprehensive benefits
- Generous access to frontier and open-weight models for day-to-day work, prototyping, and evals
Company Overview
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