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Member of Technical Staff (SWE/Product)

Organismo
Aptura
País
🇬🇧 Reino Unido
Lugar
London
Puestos
1
Tipo de vínculo
privado

Descripción del puesto

About Aptura We build the evaluation datasets and RL environments that make AI reliable in domains where mistakes are expensive: finance, healthcare, and legal. Our team designs expert-curated training data, calibrated rubrics, and verifiable task environments for AI labs and startups pushing the frontier of what models can do in regulated industries. We're a small, lean, London based team that moves fast and takes the work seriously. Everyone contributes directly. Initiative is rewarded, and ownership is the default. If you want to shape how frontier AI learns to operate in the real world, we'd like to hear from you. About the Role As a Member of Technical Staff on our Software Engineering team, you will build the platform that powers how Aptura operates and scales — the annotation tooling, workflow systems, quality control pipelines, and internal infrastructure that sit behind every dataset and environment we ship. Day to day, that looks like: designing expert task creation flows, building task assignment and review interfaces, writing the data pipelines that move outputs from domain experts into structured training sets, and integrating LLM-powered tooling directly into the product. Some days it's product engineering. Some days it's closer to infrastructure. The common thread is that the software you build is what lets a small team produce high-quality evaluation data at scale. You'll be the person who makes the platform real. Decide what gets built, how it gets built, and set the standard for how we build as we grow. What You'll Do Build and own the core data annotation and workflow platform end to end — from task assignment and expert interfaces to quality control and dataset delivery Design systems that support expert onboarding, task routing, review workflows, and QA at scale Build and improve AI-integrated pipelines across the platform, including LLM-assisted annotation, automated checking, and model-in-the-loop workflows Work directly with founders and doma

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