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Member of Technical Staff (SWE)
Отличная возможность для амбициозных инженеров поработать в перспективной нише AI Safety/Evaluation в Лондоне. Высокий балл за прямое влияние на продукт, работу с основателями и участие в создании фундаментальных технологий для ИИ.
Сложность вакансии
Высокая сложность обусловлена необходимостью работать в условиях неопределенности стартапа на ранней стадии и требованием к широкому техническому кругозору (Fullstack, Infra, AI). Ожидается полная автономность и готовность брать на себя ответственность за продукт целиком.
Анализ зарплаты
В вакансии указана конкурентная зарплата и опционы, что типично для лондонских ИИ-стартапов на ранних стадиях. Предлагаемый рыночный диапазон £70k-£110k является стандартом для опытных инженеров в этом регионе, при этом доля в капитале (equity) может значительно увеличить общую компенсацию.
Сопроводительное письмо
I am writing to express my strong interest in the Member of Technical Staff position at Aptura. Your mission to make AI reliable in high-stakes domains like finance and healthcare through expert-curated datasets and verifiable environments deeply resonates with my background in building robust, scalable software. I thrive in fast-paced, lean environments where ownership is the default and the ability to move across the stack—from product engineering to infrastructure—is essential.
In my previous experience, I have consistently demonstrated the ability to turn ambiguous requirements into high-quality, live products. I am an active user of LLMs and coding agents in my daily workflow and am excited by the prospect of integrating these tools directly into Aptura's annotation and workflow platform. I am particularly drawn to your philosophy of cutting scope rather than quality, and I am eager to contribute to a team that values speed, initiative, and end-to-end problem ownership.
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Откликнитесь в mentis уже сейчас
Присоединяйтесь к команде Aptura в Лондоне и создавайте инфраструктуру, которая сделает ИИ надежным в критически важных отраслях!
Описание вакансии
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 domain operators to turn manual, bespoke processes into reliable, scalable software
- Make strong product and engineering decisions in ambiguous, fast-moving situations — scoping, prioritising, and shipping without waiting for perfect specs
- Help define how we build as a team: tooling choices, engineering standards, and product direction
Who We're Looking For
You will not be a good fit if you thrive in well-defined scopes and prefer a clear spec before moving. We are looking for people who are comfortable identifying what needs building, making a call, and shipping — often before the full picture is clear.
You will not be a good fit if you like to go deep on one technical area for an extended period. We are looking for people who are energised by moving across product, infrastructure, data systems, and AI tooling — sometimes all in the same week.
You will not be a good fit if you prefer to hand work off at the boundaries of your role. We are looking for people who want to own problems end to end, from the first conversation through to something live.
You will not be a good fit if you aren't yet integrating AI into how you build day to day. We are looking for people who are already using LLMs and coding agents as part of how they work, and are excited to push that further.
You will not be a good fit if you think of speed and quality as things you trade off against each other. We are looking for people who don't cut corners — they cut scope.
We don't care about background, experience, or prestige. We want people who can demonstrate they will work hard, learn fast, and ship things that matter. Former founders, early engineers at startups, and people with infrastructure experience are a plus.
Nice to Have
- Experience building internal tools, annotation platforms, workflow software, or operations-heavy products
- Familiarity with modern product stacks: React, Next.js, TypeScript, Node.js, FastAPI, or similar
- Exposure to AI products, LLM tooling, or evaluation workflows
- Domain interest in finance, healthcare, or legal
- Previous experience as an early or founding engineer
On-site in London. Compensation (salary + equity) will be competitive.
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Навыки
- TypeScript
- LLM
- React
- Data Pipelines
- Infrastructure
- Node.js
- Next.js
- FastAPI
Возможные вопросы на собеседовании
Проверка способности кандидата работать в условиях неопределенности, что критично для данной роли.
Расскажите о случае, когда вам пришлось принимать архитектурное решение при отсутствии четких требований. Как вы расставляли приоритеты?
Вакансия требует навыков создания инструментов для работы с данными и ИИ.
Как бы вы спроектировали систему контроля качества для платформы разметки данных, где эксперты работают с очень сложными финансовыми или медицинскими кейсами?
Компания ищет инженеров, которые уже используют ИИ в своей работе.
Как вы используете LLM и AI-агентов в своем ежедневном процессе разработки сегодня? Приведите примеры повышения вашей продуктивности.
Роль предполагает работу на стыке продукта и инфраструктуры.
Опишите ваш опыт работы с современным стеком (React/Next.js/FastAPI). С какими самыми сложными проблемами производительности или масштабируемости вы сталкивались?
Проверка культурного соответствия принципу «владения проблемой до конца».
Расскажите о проекте, который вы вели от идеи до деплоя и последующей поддержки. С какими операционными трудностями вы столкнулись после запуска?
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