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Gimlet Labs is building a multi-silicon neocloud platform optimized for fast, efficient AI inference. The company combines large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
You will serve as an AI Developer Advocate, sitting at the intersection of engineering, product, and community. Your mission is to help developers discover, evaluate, and successfully build on Gimlet's AI inference platform. This is a hands-on technical role requiring you to create technically credible demos and educational content, engage directly with AI builders, and translate their feedback into improvements across APIs, SDKs, documentation, and product experience.
Success means developers trust your technical guidance, enjoy building with the platform, and see their needs reflected in the product roadmap. You'll be equally comfortable writing code, teaching complex technical concepts, troubleshooting with developers, and advocating internally for developer experience improvements.
In your first 6 months, you will identify the best channels for Gimlet's developer outreach and establish initial presence, build trusted relationships within relevant AI developer communities, ship useful examples and educational content that developers actively adopt, establish a reliable feedback loop between developers and the product team, and set objectives for developer experience on the upcoming developer cloud offering.
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers. You'll have the opportunity to solve hard problems, own meaningful work, build for production, and help define what's next.
REQUIREMENTS:
Must have:
- Evidence of technical communication (articles, talks, videos, documentation, open-source projects, or sample applications)
- Strong empathy for developers and instinct for recognizing friction in APIs, documentation, and tooling
- Ability to explain technical subjects clearly to audiences with different expertise levels
- Comfort engaging publicly and representing a technical product
- Ability to operate independently in a fast-moving environment
- Hands-on experience building applications with LLM or multimodal-model APIs
- Practical understanding of AI inference concepts (latency, throughput, context length, batching, quantization, model routing, cost)
- Professional software development experience, ideally with Python or TypeScript
Strong to have:
- Experience with open-weight models and serving frameworks (vLLM, SGLang, TensorRT-LLM)
- Familiarity with agent frameworks, retrieval systems, evaluations, observability, and fine-tuning
- Experience running workshops, hackathons, developer programs, or technical communities
- Contributions to open-source AI projects
- Prior experience in developer relations, developer experience, solutions engineering, or developer education
- Familiarity with GPU infrastructure and production ML systems
