Spherecast is building Agnes, the AI Supply Chain Manager for Consumer Goods.

Agnes decides what to produce, where to make it, and how to move it through factories, warehouses, and channels – simulating thousands of options in seconds and executing the best plan.

We are building Agnes to shape the physical flow of goods in the world, turning it into something as programmable as code.

When we succeed, every consumer product you touch will run on the Spherecast stack – planned in the Spherecast OS, executed over Spherecast Rails, and optimized by the shared Spherecast Network.

Oracle turned atoms into bits. Spherecast turns bits back to atoms, but globally optimized.

Be part of the next $T company.

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.

At Spherecast, we’re building the AI Supply Chain Manager that actually runs how physical goods move through the world.

On the tech side, that means:

• AI agents that act, not just predict

  Orchestrating decisions like production, transfers, and freight booking across fragmented ERPs, 3PLs, and co-manufacturers, with humans in the loop where it matters.

• High-performance, safety-critical software

  Low-latency services and data pipelines that integrate with systems like NetSuite, process multi-tenant customer data at scale, and remain robust when the real world is messy.

• Optimization & operations research in production

  Using OR, heuristic search, and constraint solving to find globally good plans (what to make, where, and when) under capacity, lead-time, and shelf-life constraints, not just local tweaks.

• Forecasting, simulation & explainability

  Probabilistic demand forecasts, scenario engines (“what if growth is +20%?”), and explainable AI so planners can see why a recommendation is safe to trust.

We’re still early. The people who join now will own major parts of this architecture and grow into the technical and product leaders who define how “AI runs supply chains” for the next century.