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ML Engineer at Passionfruit
Отличная позиция в хорошо финансируемом стартапе (Series A, $13.3M) с возможностью влиять на архитектуру. Работа с клиентами уровня Fortune 500 добавляет веса в резюме.
Сложность вакансии
Высокая сложность обусловлена требованием 4-8 лет опыта и статусом 'самого опытного ML-инженера' в команде. Необходимо глубокое понимание RAG, агентов и интеграции с маркетинговыми API.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции Senior ML Engineer в Лондоне рыночный диапазон составляет £85,000 – £120,000 плюс опционы. Стартапы стадии Series A обычно предлагают конкурентные оклады и значительную долю в капитале.
Сопроводительное письмо
I am writing to express my strong interest in the Senior ML Engineer position at Passionfruit. With over 6 years of experience in building production-grade machine learning systems and a deep focus on LLM orchestration and RAG architectures, I am excited by the opportunity to lead the development of agentic workflows for your Fortune 500 clients. My background in Python and API development, combined with a track record of deploying scalable AI solutions, aligns perfectly with your mission to automate complex marketing operations.
In my previous roles, I have successfully integrated LLMs with live data sources and built robust evaluation frameworks to ensure model reliability. I am particularly drawn to Passionfruit's elite technical environment and the challenge of moving global brands toward AI-native precision. I am confident that my expertise in agentic memory and tool-use capabilities will allow me to make an immediate impact on your high-impact team.
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Поговорите с Джеком сегодня, чтобы стать ключевым ML-инженером в перспективном стартапе Passionfruit!
Описание вакансии
This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers.
She will pick the best candidates from Jack's network
The next step is to speak to Jack.
Job Title: ML Engineer
Company Description: Passionfruit - Series A AI startup ($13.3M raised) backed by Seaya and Playfair
Job Description:
As the most senior ML Engineer on a high-impact team, you will design and ship production LLM systems that automate complex marketing operations for Fortune 500 clients like PepsiCo and HSBC. You will build agentic workflows with persistent memory and tool use, transforming how global brands analyze data and execute campaigns.
Location: London, UK
Why this role is remarkable:
- Take meaningful ownership of architecture and model selection as the most senior ML engineer on a small, elite technical team.
- Work at a well-funded Series A startup ($13.3M raised) backed by top-tier investors like Firstminute Capital and Seaya Ventures.
- Directly impact the marketing operations of some of the world's largest brands, moving them from manual "gut-feel" processes to AI-native precision.
What you will do:
- Develop and deploy production-grade ML/LLM systems and agentic workflows with persistent memory and sophisticated tool-use capabilities.
- Integrate models with live enterprise data sources including Google Ads, GA4, Meta Ads, and various CRM and analytics platforms.
- Build and refine evaluation frameworks, retrieval systems, and fine-tuning processes specifically optimized for the marketing domain.
The ideal candidate:
- Possesses 4-8 years of hands-on experience building and shipping production ML systems, with deep expertise in RAG and LLM agents.
- Demonstrates strong engineering fundamentals in Python and API development, with the ability to thrive in a fast-paced, high-ownership environment.
- Brings a proven track record of solving ambiguous technical challenges, ideally with exposure to adtech, marketing analytics, or Elixir.
Who are Jack & Jill?
Ok, I'll go first. I'm Jack, an AI that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support.
And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate.
Next steps
Step 1. Visit our website.
Step 2. Click 'Talk to Jack'.
Step 3. Talk to Jack so he can understand your experience and ambitions.
Step 4. Jack will make sure Jill (the AI agent working for the company) considers you for this role.
Step 5. If Jill thinks you're a great fit and her client wants to meet you, they will make the introduction.
Step 6. If not, Jack will find you excellent alternatives. All for free.
We never post fake jobs
This isn't a trick. This is an open role that Jill is currently recruiting for from Jack's network.
Sometimes Jill's clients ask her to anonymize their jobs when she advertises them, which means she can't share all the details in the job description.
We appreciate this can make them look a bit suspect, but there isn't much we can do about it.
Give Jack a spin! You could land this role. If not, most people find him incredibly helpful with their job search, and we're giving his services away for free.
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Навыки
- Python
- LLM
- RAG
- Machine Learning
- API Development
- Marketing Analytics
- Elixir
- Google Ads API
- GA4
- Meta Ads API
- CRM Integration
Возможные вопросы на собеседовании
Вакансия делает упор на 'agentic workflows' с памятью. Важно понять, как кандидат реализует долгосрочную память и согласованность действий агента.
Как бы вы спроектировали систему персистентной памяти для маркетингового агента, который должен помнить контекст кампаний за несколько месяцев?
Работа предполагает интеграцию с Google Ads и GA4. Нужно оценить опыт работы с внешними API и обработкой потоковых данных.
Опишите ваш опыт интеграции LLM с внешними API (tool-use). С какими основными проблемами надежности вы сталкивались?
Для Fortune 500 клиентов критически важна точность и отсутствие галлюцинаций.
Какие метрики и фреймворки оценки (evaluation frameworks) вы считаете наиболее эффективными для RAG-систем в маркетинговом домене?
Роль предполагает лидерство в архитектурных решениях.
В каких случаях вы бы предпочли дообучение (fine-tuning) модели вместо использования RAG для задач анализа маркетинговых данных?
Упоминание Elixir в описании может указывать на специфический стек или инфраструктуру.
Был ли у вас опыт работы с Elixir или функциональными языками в контексте высоконагруженных систем, и как это помогает в ML-инженерии?
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