Applied ML engineer in financial crime

Wise; Greater London; On-site; GBP 145,000 - 182,000; Full time

Benefits offered by this job

RSUs

Конкурентная компенсация

Job summary

Wise ищет Staff Applied ML Engineer, чтобы возглавить архитектуру и развивать production нейронные модели для обнаружения финансовых преступлений на глобальном масштабе. Вы будете определять архитектурную стратегию, разворачивать модели в реальном времени, создавать повторяемые пайплайны, исследовать foundation-модели и наставлять инженеров и дата-сайентистов на передовой ML.

Опыт масштабируемых ML-систем, знание PyTorch и Python, а также способность влиять на техническую стратегию —

Qualifications

• Основы глубокого обучения и теории нейронных сетей.
• Опыт развёртывания ML-систем в продакшене и под реальную нагрузку.
• Умение принимать архитектурные решения и нести ответственность за траекторію.
• Менторство инженеров и дата-сайентистов, наставничество.
• Способность к кросс-функциональному взаимодействию.

Responsibilities

• Разработка и развёртывание ML/ DL моделей для детекции финансовых преступлений в реальном времени.
• Определение архитектурной стратегии для применения ML в рисках и FinCrime.
• Построение повторяемого конвейера от экспериментов до продакшена.
• Изучение и прототипирование foundation-моделей и embedding-методов.
• Сотрудничество с Data Science по оценке моделей и дизайну экспериментов.

Skills

Deep learning fundamentals

Production ML systems

Architecture decisions

Mentoring

Cross-team collaboration

Tools

Python

PyTorch

Distributed training

ML pipeline orchestration

Graph neural networks

Job description

Описание

Wise moves billions across borders every year. Behind every transaction is a decision: is this safe? Our ML systems make that call - at scale, in real time, across every market we operate in. Our Risk ML team is building the next generation of financial crime detection at Wise - investing in modern architectures like deep learning, graph neural networks, and foundation models to detect increasingly sophisticated fraud and money laundering patterns. We're looking for a Staff Applied ML Engineer to lead this evolution: defining the architecture strategy, shipping production neural models, and building the blueprint that scales across FinCrime domains.

Задачи

• Designing and shipping ML and deep learning models for financial crime detection - sequence-based, graph-based, attention-based - serving real-time decisions at Wise's scale
• Defining the architecture strategy for how Wise applies modern ML to risk - which model families, which serving patterns, which training paradigms
• Building the reusable end-to-end pipeline pattern - from experimentation through training to production deployment - that future models follow
• Evaluating and prototyping foundation model and embedding approaches for transaction representation across FinCrime domains
• Partnering with Data Science on model evaluation, experimentation design and causal measurement in domains where clean A/B testing isn't always possible
• Mentoring engineers and data scientists on modern ML fundamentals, production best practices, and architectural decision-making

Требования

• Production experience shipping deep learning models at scale - systems serving real traffic under latency constraints
• Ability to make architecture-level decisions independently - model selection, training infrastructure, serving strategy - and explain the reasoning and tradeoffs
• Experience designing ML systems with hard latency and throughput requirements, including optimisation decisions (quantization, pre-computed embeddings, batching strategies)
• Strong fundamentals in deep learning: gradient dynamics, attention mechanisms, graph message-passing, sequence modelling
• Track record of influencing technical strategy across teams - you don't just build, you shape direction
• Python, PyTorch (or equivalent), distributed training, ML pipeline orchestration

Будет плюсом

• Experience in FinCrime, fraud detection, AML, or regulated financial services;
• Experience with graph-based methods (GNNs, entity resolution, link analysis) in production;
• Foundation model fine-tuning or LLM evaluation experience;
• Experience establishing modern ML practices in organisations scaling their ML capabilities

Условия

Starting salary: £145,000 - £182,000 + RSUs