AI Systems Architect

🕒 July 27

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

⚙️ Systems Engineer

👻 Ghost score 18%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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TubeScience

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2016

📣 Marketing

📱 Media

🤝 B2B

Marketing • Media • B2B

TubeScience is a performance-driven creative agency that produces and scales video-first advertising for brands using a pay-for-performance model. Built as Meta’s largest creative partner, TubeScience combines strategy, in-house production (100,000 sq ft Los Angeles studio), and expert performance media buying to create and test platform-native ads across social, streaming, and TV/CTV. The company also operates TubeScience Labs—an applied-AI team that builds internal tooling (creative intelligence and an AI brand/legal review platform called Flawless) to accelerate iteration and ensure regulatory and brand fidelity. TubeScience serves enterprise and growth-stage clients across ecommerce, D2C, and regulated industries and emphasizes measurable outcomes (ROAS/CPA) with a budget-allocation model that funds only top-performing ads.

📋 Description

• Own the infrastructure core: Set the technical strategy for the media pipelines, LLM routing, distributed data systems, and observability tooling that the entire platform runs on — and architect them from first principles. • Design AI agent systems: Build AI agents and multi-agent systems from the ground up — orchestration, tool-calling, MCP servers, A2A dispatch, and the permissions and observability infrastructure that makes them reliable in production. • Go deep on distributed systems: Push into the hard parts — data flow, fault boundaries, consistency trade-offs, performance under load — to build infrastructure that holds up when it matters. • Raise the bar on reliability: Design for observability from day one — tracing, metrics, structured logging — and build the quality systems that let a fast-moving team move without breaking things. • Shape the roadmap: Bring an engineering point of view to platform strategy — what we should build, in what order, and why.

🎯 Requirements

• Have set technical direction across teams, and can point to specific systems you architected and operated in production, and the moments where you brought people along. • Are deeply expert in distributed systems including the layers below the framework — data flow, fault tolerance, consistency, and performance under real load. • Design AI agents and agentic systems: orchestration, tool-calling, multi-agent coordination, and the infrastructure that makes them observable and production-ready. • Have built large-scale platforms from scratch — making the foundational decisions and living with them — not just joined a mature codebase. • Design clean, durable APIs across REST, gRPC, event-driven, and schema-based systems, and are fluent with MCP, A2A, schema registries, and tool-calling normalization. • Know media pipelines deeply — transcoding, ffmpeg-class tooling, cloud storage, large-object processing — with strong hands-on depth across PostgreSQL, OpenSearch, Redis, Docker, Kubernetes, and Terraform or Pulumi. • Are the person teams want in the room when a hard architectural call has to be made.

🏖️ Benefits

• Competitive compensation • Comprehensive benefits • Generous access to frontier and open-weight models for day-to-day work, prototyping, and evals