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Head of Machine Learning (Remote - UK/Europe/Americas)

Оценка ИИ

Отличная вакансия в зрелом стартапе с сильной инженерной культурой, возможностью удаленной работы и фокусом на создание собственных ML-технологий.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена необходимостью совмещать глубокую техническую экспертизу в ML (обучение моделей с нуля) с управлением командой из 10 человек и координацией кросс-функциональных проектов.

Анализ зарплаты

Медиана140 000 £
Рынок110 000 £ – 180 000 £
Оценка ИИ

Предлагаемая роль Head of ML в британском стартапе такого уровня обычно предполагает зарплату выше среднего по рынку, дополненную значительным пакетом опционов. Указанные рыночные оценки отражают уровень компенсации для Senior Management позиций в сфере AI в Великобритании и Европе.

Сопроводительное письмо

I am writing to express my strong interest in the Head of Machine Learning position at Mimica. With a robust background in leading high-performing ML teams and a deep technical foundation in building proprietary models from the ground up, I am drawn to Mimica’s mission of reclaiming human time through intelligent task mining. My experience in scaling engineering chapters and aligning complex ML initiatives with product OKRs directly matches the requirements of this role.

Throughout my career, I have focused on fostering cultures of technical excellence and continuous learning, much like the environment you have built at Mimica. I am particularly impressed by your commitment to training models from scratch rather than just consuming existing APIs. I am confident that my expertise in MLOps, distributed systems, and people management will allow me to effectively lead your 10-person ML chapter and contribute to the strategic evolution of your process intelligence platform.

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Откликнитесь в mimica уже сейчас

Присоединяйтесь к Mimica в качестве Head of ML и возглавьте команду, создающую будущее автоматизации бизнес-процессов!

Описание вакансии

What we are building

Mimica's mission is to empower enterprises, teams, and individuals to reclaim their most precious resource — time and work more efficiently, with greater purpose and impact.

Our AI-powered task mining observes employee actions across the desktop and categorizes them into detailed process maps. Mimica’s process intelligence highlights inefficiencies, prioritizes improvements based on ROI, recommends the optimal technology for automation (RPA, intelligent document processing, GenAI), and provides a blueprint for building new automations and transforming work.

Where we are in our scale-up journey

We’ve achieved strong product-market fit and are now scaling with intention. You’ll join a 45-person team of Senior+ engineers and Product leaders who prioritise product impact over mere headcount. We have 8 cross-functional teams, including a dedicated Platform team focused on infra & developer experience. We started building proprietary ML before the LLM boom, so we don't just consume models - we train them from the ground up.

Our approach to engineering

  • We prioritize customer needs first
  • We work in small, project-based teams
  • We have flexibility in terms of the problems we work on
  • We own the full lifecycle of our projects
  • We avoid silos and encourage taking up tasks in new areas
  • We balance quality and velocity
  • We have a shared responsibility for our production code
  • We each set our own routine to maximize our productivity

Your mission

You will join us as our Head of Machine Learning, and report to our CTO. You’ll manage a Chapter of 10 Machine Learning Engineers, including 3 Team Leaders and expand the team. You’ll have a mix of People Management and project coordination responsibilities.

You'll understand the strategic direction of the ML team's projects, with the intention to coordinate projects, dependencies, allocate resources, and ensure strategic alignment with business and product goals.

You'll contribute to the architecture and development of our systems by empowering the team through 1:1s, code reviews, and discussions to facilitate the delivery of impactful features. Your leadership will foster a culture of growth, efficiency, and technical excellence, driving the execution of key ML initiatives that support the company’s broader vision.

Part of your day-to-day

  • Lead and nurture a growing team of machine learning engineers, supporting their career development through coaching and mentorship.
  • Leading team OKR discussions, coordinating projects and facilitating team meetings, planning and retros.
  • Collaborate with the CTO, Platform and Product Managers to align team priorities with company OKRs.
  • Collaborate with the People team on recruiting and onboarding talent that matches our values and technical excellence.
  • Act as a sounding board for the team, empowering the team, and support identifying and resolving bottlenecks and efficiency blockers, enabling the team to iterate faster.
  • Drive the development and deployment of ML systems, optimising tools and infrastructure for efficiency, while ensuring timeline and goals are met.
  • Promoting a culture of collaboration and continuous learning, and mentoring team members in their development.

Requirements

  • Strong background in applied AI/ML research, development, and deployment
  • Significant experience in leading and executing machine learning initiatives, particularly in high-growth and large-scale product companies.
  • Proven track record in managing and growing ML Engineers/Data Scientist teams, including hiring, mentoring, and developing talent.
  • Deep understanding of ML engineering practices, including MLOps and data engineering.
  • Expertise in collaborating with Product and Engineering teams to align ML efforts with broader product goals.
  • Strong communication skills to engage with senior leaders, product teams, and engineers in complex technical discussions.
  • Strong analytical and troubleshooting skills - methodically decomposing systems to identify bottlenecks, determine root causes and implement effective solutions.
  • Drive to continually develop your skills, improve team processes and reduce debt.
  • Fluency in English, with effective communication skills – articulating complex ideas, concepts, and trade-offs clearly and getting buy-in for strategic technical decisions.

Bonus

  • Background in successful startups/scale-ups, driving iterative development and rapid delivery.
  • Experience working in a distributed systems environment.
  • Experience with general software design and data protection mechanisms.

What we offer

💰 Generous compensation + stock options - aligned with our internal framework, market data, and individual skills.

🏢 Distributed work: Work from anywhere - fully remote, in our hubs, or a mix.

💻 Company-issued laptop, remote setup stipend, and co-working budget

📍 Flexible schedules and location

☀️ Ample paid time off, in addition to local public holidays

🍼 Enhanced parental leave

🧘‍♀️ Health & retirement benefits

📖 Annual learning & development budget

🌴 Annual workaways and regular virtual & in-person socials

🌍 Opportunity to contribute to groundbreaking projects that shape the future of work

*Note: Some benefits may vary depending on location and role*

⚠️ Mimica will only contact candidates from an @mimica.ai email address. We do not request banking or sensitive personal information during the recruiting process.

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Создайте идеальное резюме с помощью ИИ-агента

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Навыки

  • Project Management
  • Strategic Planning
  • Python
  • Machine Learning
  • Data Engineering
  • MLOps
  • Distributed Systems
  • People Management

Возможные вопросы на собеседовании

Проверка опыта масштабирования команд и найма в условиях быстрого роста.

Расскажите о вашем опыте найма и развития ML-инженеров: какие критерии вы считаете ключевыми при отборе Senior+ специалистов?

Оценка способности выстраивать процессы разработки и доставки ML-решений.

Как вы организуете жизненный цикл ML-проекта от исследования до деплоя, чтобы сбалансировать качество моделей и скорость вывода на рынок?

Проверка навыков стратегического планирования и работы с OKR.

Как вы расставляете приоритеты в ML-команде, когда возникают конфликты между долгосрочными исследовательскими задачами и краткосрочными потребностями продукта?

Оценка технического лидерства в области инфраструктуры.

Каков ваш подход к построению MLOps инфраструктуры для компании, которая обучает собственные проприетарные модели, а не просто использует готовые API?

Проверка навыков решения конфликтов и управления производительностью.

Опишите случай, когда ML-проект столкнулся с серьезным техническим блокером. Как вы помогли команде его преодолеть?

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mimica
Страна
Великобритания