Agent Infrastructure Engineer
Posted 18 Aug 2026
Advertised as “Agent Infrastructure Engineer — Core Harness (Superagent)”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
No salary listed for this role.
About ImagineArt
We're redefining how the world creates and designs.
ImagineArt is one of the fastest-growing GenAI companies in the world. We've scaled faster than most funded startups — with zero outside funding.
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100M+ social impressions
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Built and shipped our own image generation model, now ranked #3 globally for photo realism
No funding. No shortcuts. Just a sharp, driven team building one of the strongest GenAI products in the world — and we're just getting started.
We're looking for an Agent Infrastructure Engineer to own Superagent, our core agent harness that powers conversations, tool calls, and multi-step agentic workflows across our AI products.
This is a deep systems and infrastructure role — not prompt engineering and not simply wrapping model APIs. You'll work on the core orchestration loop, tool-calling infrastructure, context and memory management, streaming, retries, evaluation, observability, and performance.
Key Responsibilities
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Own the architecture, development, and evolution of Superagent, our core agent harness.
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Design and optimize the agent execution loop for latency, reliability, token efficiency, cost, and task completion.
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Build and improve core harness systems including context management, memory/state handling, tool routing, function schemas, structured outputs, retries, and error recovery.
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Build and maintain agent evaluation infrastructure to measure quality and guide engineering decisions with data.
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Integrate and benchmark multiple LLM providers and models, evaluating performance, cost, reliability, and capabilities.
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Implement performance optimizations such as caching, batching, parallel tool execution, and prompt/context compression.
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Build deep observability and instrumentation across agent runs, including tracing, logging, metrics, and regression detection.
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Extend and customize underlying agent frameworks when existing abstractions are insufficient.
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Build reliable integrations with evolving AI and tool ecosystems.
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Work closely with product engineering teams to expose clean abstractions while keeping harness complexity behind the platform.