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A technology company is seeking an AI Engineer to enhance its platform with large language model capabilities. This fully remote role involves designing prompts, implementing LLM-powered features, and evaluating model performance. Candidates should have a strong coding background, especially in TypeScript, and excel in asynchronous communication within a distributed team.
ABOUT THIS ROLE
As an AI Engineer at Reforge, you will play a key role in shaping how our platform uses large language models to deliver value to customers. You’ll focus on building and improving LLM-powered products and features – from crafting high-quality prompts to evaluating and tuning model outputs for optimal performance. This is a highly collaborative, product-focused role where you’ll work closely with product managers and engineers to turn cutting-edge AI capabilities into practical features. This position is fully remote, so we’re looking for someone who is self-motivated and excels at clear, asynchronous communication within a distributed team.
WHO YOU'LL WORK WITH
This is a highly collaborative, product-focused role where you’ll work closely with product managers and engineers to turn cutting-edge AI capabilities into practical features. This position is fully remote, so we’re looking for someone who is self-motivated and excels at clear, asynchronous communication within a distributed team.
WHAT YOU'LL OWN
Prompt Engineering: Design, experiment with, and refine prompts and system instructions to maximize the effectiveness and reliability of LLMs across Reforge’s products.
LLM-Powered Features: Collaborate with product and engineering teams to develop and integrate new backend features powered by LLMs, enhancing our Insight Analytics platform with intelligent capabilities.
Continuous Improvement: Continuously evaluate the quality of LLM outputs via internal testing and user feedback. Iterate on prompts, model settings, or data pipelines to improve performance over time and deliver a better user experience.
Model Evaluation: Design and implement evaluation frameworks to assess LLM performance across different use cases. Monitor key metrics and develop automated testing pipelines to ensure consistent quality.
Coding & Integration: Write and maintain code (primarily in TypeScript, with some Python) to implement support for LLM interactions – including building retrieval-augmented generation (RAG) pipelines, working with vector embeddings, and handling content chunking of large documents. A majority of your work will be in-code shipping product — not in prompting tools or notebooks.