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You will build evaluation pipelines and benchmarks to measure AI system performance while turning research prototypes into production-ready workflows. Additionally, you will manage cloud infrastructure, backend services, and APIs to support the deployment of AI-driven legal products.
Join a distributed team working at the intersection of AI research and product development. You will turn AI experiments into reliable products for legal work, building the evaluation systems, services, and features needed to bring applied AI into production.
Build repeatable benchmarks and evaluation pipelines to measure accuracy, hallucinations, retrieval quality, latency, and cost.
Create dashboards that help the team understand changes in system performance.
Turn research prototypes into usable retrieval, drafting, document, and agent workflows.
Build backend services, APIs, and internal tools for testing and releasing improvements.
Deploy and operate cloud infrastructure, including monitoring, databases, delivery pipelines, and reliable operations.
Work closely with AI research while staying connected to product and user needs.
At least 3 years of experience as a software, infrastructure, or product engineer building and deploying production systems.
Strong Python skills and hands-on cloud deployment experience.
Experience building evaluation pipelines, benchmarks, or assessment systems for machine learning or AI systems.
Experience with APIs, databases, backend services, deployment pipelines, and dashboards or monitoring systems.
Ability to turn experimental code and ambiguous ideas into maintainable products.
Interest or experience in LLMs, retrieval-augmented generation, agents, AI benchmarks, or document automation.
Full-stack experience is a plus. Familiarity with FastAPI, Docker, React, Next.js, Postgres, or vector databases is helpful.
Annual base salary is $180,000 to $260,000 USD, depending on relevant skills, experience, and role scope. Visa sponsorship is not available.
Remote within the United States.
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