Staff Applied ML Engineer — Financial Crime Risk

SmartRecruiters, Inc.; Greater London; On-site; GBP 145,000 - 182,000; Full time

Job summary

Wise is hiring a Staff Applied ML Engineer to lead the next generation of financial crime detection. You will own architecture decisions, ship production neural models and scale ML across FinCrime domains.

You will mentor engineers, collaborate with data scientists, and shape the ML foundation for real-time decisions at Wise. This is a greenfield opportunity with strong senior leadership support and a focus on scalable, production-grade ML systems.

Qualifications

• Production experience shipping deep learning models at scale.
• Ability to make architecture-level decisions independently.
• Experience designing ML systems with hard latency/throughput requirements.
• Track record of influencing technical strategy across teams.
• Proficiency with Python and PyTorch.

Responsibilities

• 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 - model families, serving patterns, training paradigms
• Building reusable end-to-end pipeline patterns from experimentation to production deployment
• 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
• Mentoring engineers and data scientists on modern ML fundamentals, production best practices and architectural decision-making

Skills

Deep learning

Architecture decisions

Python

PyTorch

ML pipelines

Production ML

Tools

Git

Job description

Wise is hiring a Staff Applied ML Engineer to lead the next generation of financial crime detection. You will own architecture decisions, ship production neural models and scale ML across FinCrime domains.

You will mentor engineers, collaborate with data scientists, and shape the ML foundation for real-time decisions at Wise. This is a greenfield opportunity with strong senior leadership support and a focus on scalable, production-grade ML systems.