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(RD) Staff Machine Learning Engineer
Отличная позиция для опытного инженера: работа с передовыми технологиями (LLM, Agents) на огромном масштабе в известном финтех-единороге. Гибридный формат в Амстердаме и сильная инженерная культура делают вакансию очень привлекательной.
Сложность вакансии
Роль уровня Staff/Principal требует не только глубоких знаний в области LLM и MLOps, но и способности проектировать архитектуру систем для 150+ млн пользователей. Высокая планка ответственности за финансовые решения и необходимость менторства делают эту позицию крайне сложной.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции уровня Staff ML Engineer в Амстердаме рыночный диапазон составляет €100,000 – €140,000 в год плюс бонусы и опционы. Backbase обычно предлагает конкурентоспособные пакеты, соответствующие верхнему децилю рынка.
Сопроводительное письмо
I am writing to express my strong interest in the Staff Machine Learning Engineer position at Backbase. With over five years of experience in designing and deploying large-scale ML systems, I am particularly drawn to Backbase's mission of building an AI-native Banking OS. My background in developing production-grade models and implementing RAG and agent-based systems aligns perfectly with your goal of moving from fragmented systems to unified, AI-driven banking experiences.
In my previous roles, I have successfully managed the entire ML lifecycle, from feature engineering to MLOps and real-time monitoring. I am impressed by Backbase's high-ownership culture and the scale at which your models operate, touching the lives of 75 million users. I am confident that my technical expertise in Python, PyTorch, and streaming systems like Kafka, combined with my ability to mentor engineers and communicate complex concepts, will allow me to make a significant contribution to your RnD department in Amsterdam.
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Присоединяйтесь к команде Backbase и создавайте AI-решения, которыми будут пользоваться миллионы клиентов крупнейших банков мира!
Описание вакансии
The Job in short
Over 75 million people interact with banking products built on Backbase. Every model you ship here touches real accounts, real decisions, and real financial lives - at scale. That's the standard this role is built around.
Backbase leads in AI-native banking technology, helping the world's biggest banks move from fragmented systems to unified frontlines where humans and AI agents work together. We power 100+ of the largest banks globally, and our AI capabilities sit at the core of that shift.
As a Principal Machine Learning Engineer, you will define how intelligent systems operate in this environment: not just predicting outcomes, but making safe, auditable, and real-time decisions within a controlled execution model.
Reasons to build with us
Real-world impact at scale: Your models run in production for 150+ million end users across the globe's leading banks.
AI at the core: We're building the AI-native Banking OS from the ground up - ML engineering here shapes the product direction, not just the feature roadmap.
High-ownership culture: We ship early, iterate fast, and trust engineers to make decisive calls. You'll move at speed without layers of approval slowing you down.
"We don't debate AI in the abstract - we build it and put it in front of real banks. The engineers who thrive here are the ones who start with the problem, own the outcome, and raise the bar every sprint. That's the culture we've built, and it shows in what we ship.
- VP of Engineering, AI & Data
Meet the job
You'll be a key architect of Backbase's AI capabilities. That means moving fast, owning your work end-to-end, and building ML systems that solve real problems for banking partners - not theoretical models that gather dust.
That means:
● Designing, developing, and deploying production-grade ML models that personalize the banking experience and automate complex financial workflows
● Partnering with product and data teams to integrate AI capabilities directly into the Banking Platform
● Taking full ownership of the end-to-end ML lifecycle - from data discovery and feature engineering through to model monitoring and retraining
● Turning complex data architectures into clean, maintainable ML pipelines and APIs
● Running rigorous code reviews and mentoring mid-level engineers to keep raising the technical bar
● Identifying opportunities to apply AI where it removes friction and adds measurable value for end users
How about you
You're a seasoned engineer who thrives in a straight-talk culture. You don't settle for the industry standard - yesterday's best work is today's baseline, and you're always looking for what's next. Deep technical chops and a customer-first mindset aren't in tension for you; they're the same thing.
Your track record also shows:
● 5+ years designing and deploying large-scale ML systems in production environments
● Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, etc.)
● Experience with LLMs, RAG, or agent-based systems in production
Solid MLOps experience:
CI/CD for ML
model versioning
monitoring and observability
● Experience with data and streaming systems (e.g., Spark, Kafka)
● The ability to communicate complex technical concepts clearly to non-technical stakeholders, without oversimplifying or over-complicating
● A proven ability to make decisive technical choices, meet deadlines, and keep momentum in a fast-moving environment
Why Backbase
Backbase is where ambitious engineers come to do the most consequential work of their careers. We're growing fast, we build in the open, and we hold ourselves accountable to outcomes that show up in the real world - not just on slides.
We also offer:
● Competitive salary and performance-based bonus
● Flexible working arrangements and a hybrid setup
● Access to top-tier tools, cloud infrastructure, and ML platforms
● A learning budget to keep your skills sharp and your career moving
Collaborative teams across Amsterdam, Atlanta, Bangalore, and beyond
● A culture where straight talk is valued and good ideas win regardless of where they come from
● The chance to build AI systems that run at the scale of global banking - and see the results
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Python
- PyTorch
- LLM
- CI/CD
- MLOps
- RAG
- Feature Engineering
- Kafka
- Spark
- TensorFlow
Возможные вопросы на собеседовании
Проверка опыта работы с современными архитектурами в продакшене.
Расскажите о вашем опыте внедрения RAG или агентных систем: с какими основными проблемами производительности или точности вы столкнулись?
Важно для банковской сферы, где ошибки стоят дорого.
Как вы обеспечиваете аудируемость и безопасность решений на базе LLM при работе с чувствительными финансовыми данными?
Оценка навыков системного дизайна и масштабирования.
Опишите архитектуру ML-пайплайна, который вы проектировали для обработки данных в реальном времени (например, с использованием Kafka или Spark).
Проверка соответствия культуре высокой ответственности (high-ownership).
Приведите пример, когда вам пришлось принять сложное техническое решение в условиях сжатых сроков. Каков был результат?
Оценка лидерских качеств и способности развивать команду.
Как вы подходите к проведению код-ревью и менторству мидл-разработчиков для повышения общего технического уровня команды?
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