Systems Engineer

TubeScience • International Falls, MN

4h • Remote

About The Position

TubeScience is one of the fastest-growing performance video companies globally, utilizing data, AI, and creative systems to produce video advertising at an unparalleled scale and speed. Their engineering team is responsible for building the infrastructure that enables this, from AI agent pipelines to ad operations tooling that manages significant client spend. The company is profitable, experiencing rapid growth, and offers genuinely challenging technical problems. The Systems Engineer will be responsible for owning the infrastructure that ensures the reliability of the AI platform in production. This includes media pipelines, LLM routing layers, distributed data systems, and observability tooling. This role is not about executing pre-defined architectures; rather, it involves being one of a small team of engineers who design and build these critical systems, with a focus on fast shipping, short feedback loops, and direct business impact. The position is fully remote, requiring meaningful overlap during afternoon hours in Eastern Europe timezones (CET/EET).

Requirements

• You instrument and observe distributed systems end to end — not just reading dashboards, but designing for observability from the start
• You've built and operated data engineering and ETL pipelines in production
• You design clean, durable APIs across REST, gRPC, event-driven, and schema-based systems — comfortable with MCP, A2A, schema registries, and tool-calling normalization
• You know media pipelines: transcoding, ffmpeg-like tooling, cloud storage, and large-object processing
• Solid hands-on experience with PostgreSQL, MongoDB, Redis, and cloud storage-backed systems
• Comfortable with Docker, Kubernetes, Terraform, or Pulumi

Nice To Haves

• Experience integrating LLM/AI systems in production. You have worked with LLM APIs, provider routing, multi-provider abstraction, schema normalization across providers, structured output, semantic caching, agentic AI systems, and prompt engineering at the infrastructure layer.

Responsibilities

• Media ingestion platform — taking raw uploads all the way through validation, transcoding, staging, and lifecycle-aware storage management.
• LLM infrastructure layer — routing requests across providers, normalizing outputs, applying semantic caching, and keeping things reliable under load.
• Data platform — ingesting and indexing multi-modal data, powering retrieval and search, managing context assembly, and maintaining long-term data health.
• Agent and platform operations layer — MCP servers, A2A dispatch, permissions infrastructure, and the observability layer that keeps everything visible and accountable.

Benefits

• Exceptional growth in a contracting market
• Combine startup agility with established financial stability
• Work on exciting challenges with a brilliant team of like-minded professionals who value both technical excellence and creative problem-solving
• Immediate impact on critical systems
• Clear paths for professional advancement

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed
• Number of Employees: 101-250 employees