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Ultra is seeking a software engineer to build internal AI tools and deliver backend services and interfaces quickly. You will work on agent/LLM tooling that real teams rely on, turning messy workflows into scoped, useful solutions.
Clear writing matters as much as code, and you’ll collaborate with non engineers to ensure adoption and reliable use across the company.
This role asks for a software engineer who builds internal AI tools and gets colleagues to actually use them. A strong candidate ships backend services and interfaces quickly, has built agent or LLM tools that real people relied on, and can turn a messy workflow into something scoped and useful. Clear writing and comfort working with non engineers matter as much as the code.
The job exists because AI is changing how software gets written and how other teams get work done, so the role is about making that shift real inside one company. Judgement about when to build, buy, or use a plain script stays human, as does earning trust and driving adoption. The work is measured by whether tools are used and trusted, not just shipped.
A candidate would gain hands on practice building agent infrastructure, model integrations, and shared context for a company deploying industrial robots in warehouses. That means learning how to make agents reliable in a real codebase and how to evaluate and roll out tools safely. Experience with permissions, secrets, and measuring adoption would grow here.
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
Ultra makes OP1, a multi-purpose AI robot for industrial warehouse work, per the company.