Staff Applied ML Engineer — Real-Time FinCrime

Wise; Greater London; Sur place; GBP 145 000 - 182 000; Plein temps

Avantages offerts par ce poste

Wise Benefits

RSUs

Résumé du poste

Wise moves billions across borders and is building the next generation of financial crime risk detection. We are hiring a Staff Applied ML Engineer to lead architecture, ship production neural models, and scale ML across FinCrime domains.

Based in London, you will work with data scientists and platform engineers, own end-to-end ML solutions, and mentor teams on best practices for production ML at scale, security, and latency requirements.

Qualifications

• Production experience shipping DL models at scale with real traffic and latency constraints.
• Ability to make architecture-level decisions independently and justify tradeoffs.
• Experience designing ML systems with hard latency and throughput requirements.
• Strong fundamentals in deep learning: gradients, attention, graph message-passing, sequence modelling.
• Track record of influencing technical strategy across teams.
• Proficiency in Python, PyTorch, distributed training, and ML pipelines.

Responsabilités

• Design and ship ML and deep learning models for risk detection at scale.
• Define architecture strategy for applying modern ML to risk domains.
• Build end-to-end ML pipelines from experimentation to production.
• Evaluate foundation models and embedding approaches for transaction representation.
• Collaborate with Data Science on model evaluation and experimental design.
• Mentor engineers and scientists on ML fundamentals and production practices.

Connaissances

DL models at scale

Architecture decisions

Latency/Throughput optimization

Deep learning fundamentals

Tech leadership

Python & PyTorch

Outils

PyTorch

Distributed training frameworks

Description du poste

Wise moves billions across borders and is building the next generation of financial crime risk detection. We are hiring a Staff Applied ML Engineer to lead architecture, ship production neural models, and scale ML across FinCrime domains.

Based in London, you will work with data scientists and platform engineers, own end-to-end ML solutions, and mentor teams on best practices for production ML at scale, security, and latency requirements.