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Founding LLM Engineer

Spherecast | San Francisco, California, United States | 2mo ago
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Original posting (closed) below
$110,000 – $200,000/yr| full-time | on-site | lead | 1+ years
skills: llm apis, anthropic, openai, deepseek, openrouter, gemini, moonshot, huggingface, llm-powered automations, agents, prompting, system design, llm observability, self-hosted llms

Founding LLM Engineer – Agnes

As a LLM Engineer at Spherecast, you will be responsible for building Agnes from the ground up – our AI Supply Chain Manager that decides what to produce, where to make it, and how to move it through factories, warehouses, and channels.

This is a fast-paced, highly autonomous role for someone who can own AI systems end-to-end: from prototypes to production, from prompts to tested and evaluated pipelines, from agents to real-world outcomes (POs, TOs, bookings). You’ll work directly with the core team to turn the physical flow of goods into something as programmable as code.

If you’re a builder who thrives at the intersection of LLMs, agents, systems engineering, and messy real-world data, this is your opportunity to shape how global brands run their supply chains.

What we’re looking for

You are a great fit if you have:

  • Hands-on experience with modern LLM APIs (e.g. Anthropic, OpenAI, DeepSeek, OpenRouter, Gemini, Moonshot) and have shipped features using them.
  • Strong intuition for large language model selection – you understand the strengths, weaknesses, latency/cost tradeoffs, and ideal use cases of different LLMs.
  • Practical experience with the HuggingFace ecosystem in real projects.
  • A track record of building LLM-powered automations or agents that are core to a production system, not just internal demos or playgrounds.  
  • Experience designing and maintaining evaluation pipelines to iterate quickly and safely on prompts and workflows.  
  • Strong prompting and system-design skills – you know how to design tools/functions, structured outputs, and multi-step agent flows that are robust to edge cases.  
  • Experience with LLM observability and monitoring (logging, traces, quality metrics, feedback loops) to track and improve production accuracy over time.  
  • Bonus: Experience running self-hosted LLMs in production or serious prototypes.

Benefits

health insurance
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