AI Platform Engineer, Agent Systems IRC

Description

The company is building the agent platform for professional music production: the orchestration layer, tool interfaces, skills runtime, and context architecture that allow any AI agent to reason about and act on a music‑production workflow.

You will lead the design of the orchestration loop, define how the engine's capabilities are exposed to models, build the skills runtime that transforms a general‑purpose model into a domain specialist, and architect the context and memory systems that keep agents coherent across long creative sessions.

The object model is a song. The users are producers, musicians, and creatives. The domain has real‑time constraints, deep semantics, and no existing playbook.

• Five or more years shipping production platform or infrastructure software that other engineers have built on top of.
• Eighteen or more months of production experience building LLM agent systems, covering orchestration loops, tool use, and context management. Preference is given to engineers who can articulate what they learned from different approaches.
• Demonstrated experience designing tool interfaces for LLM consumption, explaining what makes a tool schema discoverable and usable by a model versus technically correct.
• Demonstrated experience building context, memory, or state‑management systems beyond framework defaults, including compaction, durable memory, or session persistence. You have diagnosed agent failures from raw execution traces and made targeted harness changes in response.
• Strong proficiency in Type Script and Python.
• Experience with the Model Context Protocol (MCP) or similar tool‑connectivity standards.

• Background in music production, audio engineering, or another creative‑tool domain, including as a serious hobbyist.
• Experience with real‑time audio systems, professional audio software, or other latency‑sensitive environments.
• Experience making a complex desktop or professional application agent‑accessible, in any domain with a rich object model (DAW, IDE, design tool, CAD).
• Experience building middleware or hook architectures that allow others to customize agent behavior without modifying core code.

• Tool interfaces. Define how the engine's capabilities are exposed to LLMs as structured, discoverable tools. This includes schemas, semantic descriptions, scoped tool sets, input validation, and output parsing that a model can reliably produce and the harness can reliably consume.
• Orchestration and control flow. Design and build the harness: the core loop and the machinery around it, covering step sequencing, retries, timeouts, error recovery, fallback paths, and multi‑agent coordination where a workflow is split across sub‑agents. You will evaluate whether to build this in‑house, adopt a framework, or extend an existing one.
• Skills runtime. Design the format, packaging, loading, and execution layer for the structured domain knowledge that turns a generic model into a music‑production specialist.
• Context, memory, and state. Build the systems that keep agents performant and coherent across long, multi‑step creative workflows, including context compaction, short‑term working memory, durable cross‑session memory, session state persistence, continuity across disconnects, and sub‑agent delegation.
• Extension points. Design the harness so that new tools, skills, and middleware can be added without modifying the core runtime.
• Evaluations, observability, and failure analysis. Build and own the platform‑level evaluation surface, the observability that every engineer on the platform depends on, and the feedback loop that converts failed agent runs into targeted harness changes.
• Ongoing simplification. Audit the harness on a regular basis and remove components that models no longer require.

• LLM integration engineering – this role is not responsible for wiring models to the DAW or building end‑user AI features.
• ML or model engineering – the team does not train models.
• Research – the team applies current research in production; original research happens elsewhere in the company.

We provide a comprehensive benefits package that includes competitive compensation and support for overall well‑being.

We foster continuous learning and professional development with dedicated learning resources.

We prioritize work‑life balance through flexible work arrangements and support for relocation and rotation options.

We value diversity, equity, and inclusion, providing equal opportunities for all individuals in an inclusive work environment.