Builds scalable AI-powered research tools, including full stack agent behaviors with LLMs, RAG, and agent architectures. Owns prompt engineering, model deployment, and eval pipelines while engaging customers and optimizing performance in a fast-paced startup.
150k – 200k/yr
On-siteFullstack Engineering
About the role
Responsibilities
Design and build scalable features and infrastructure for AI-powered research tools.
Engage directly with customers to gather insights and improve user experience.
Monitor and optimize system performance and costs, making pragmatic tradeoffs.
Establish best practices for prompt engineering and deployment of new models and workflows.
Own the design and automation of eval pipelines to measure, benchmark, and improve AI agent quality and reliability.
Requirements
Quick, scrappy builder who moves fast from idea to production.
Blend of excellent engineering, models, and design taste.
Experience with full stack agent behaviors, LLMs, RAG, and agent architectures.
Independently built and scaled a product to thousands of users (side project or open-source).
Experience in high-growth, fast-paced environment (ideally startup) with little oversight.
Build and ship a live deployment knowledge graph and agentic software systems that automate AI supercomputer site bring-up, contractor workflows, QA inspections, and as-builts. Forward-deploy alongside field engineers on live construction sites to turn their domain expertise into structured, queryable data models and autonomous planning tools. Requires production experience with Go/Python/TypeScript, LLM APIs, and agentic coding tools.
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