{"id":1148220,"url":"https://alion.io/job/wise-staff-applied-ml-engineer-financial-crime-3","title":"Staff Applied ML Engineer - Financial Crime","company":{"id":3856,"name":"Wise","domain":"wise.com","url":"https://alion.io/company/wise","size_band":"1001-5000","is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":80,"open_postings":27,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":21,"computed_at":"2026-09-23T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":145000,"max":182000,"currency":"GBP","period":"year","gross":null,"usd_annual":241800},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Embeddings","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Fine-tuning","optional":true},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-09-23T15:32:59Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-09-24T00:40:24Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Wise is a global technology company, building the best way to move and manage the world’s money.\nMin fees. Max ease. Full speed.\nWhether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.\nAs part of our team, you will be helping us create an entirely new network for the world's money.\nFor everyone, everywhere.\nMore about our mission and what we offer.\n About the role:\nWise 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.\nOur 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.\nThis is a greenfield opportunity - you'll be setting the direction for how Wise applies modern ML to financial crime risk, with strong investment and engagement from senior leadership.\nHow we work:\nRisk ML sits within Wise's FinCrime organisation, owning the full ML and AI foundation for financial crime detection. We're scaling into three dedicated pillars - Feature Platform, Learning Loop and Risk Modelling. You'll sit in Risk Modelling, working alongside data scientists, platform engineers, product and domain experts.\nWe operate with high autonomy and low hierarchy. You'll own problems end-to-end - from research and architecture decisions through to production deployment and impact measurement. We value engineers who shape direction, not just execute tickets.\nWhat will you be working on?\nDesigning 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\nDefining the architecture strategy for how Wise applies modern ML to risk - which model families, which serving patterns, which training paradigms\nBuilding the reusable end-to-end pipeline pattern - from experimentation through training to production deployment - that future models follow\nEvaluating and prototyping foundation model and embedding approaches for transaction representation across FinCrime domains\nPartnering with Data Science on model evaluation, experimentation design and causal measurement in domains where clean A/B testing isn't always possible\nMentoring engineers and data scientists on modern ML fundamentals, production best practices, and architectural decision-making\nWhat do you need?\nProduction experience shipping deep learning models at scale - systems serving real traffic under latency constraints\nAbility to make architecture-level decisions independently - model selection, training infrastructure, serving strategy - and explain the reasoning and tradeoffs\nExperience designing ML systems with hard latency and throughput requirements, including optimisation decisions (quantization, pre-computed embeddings, batching strategies)\nStrong fundamentals in deep learning: gradient dynamics, attention mechanisms, graph message-passing, sequence modelling\nTrack record of influencing technical strategy across teams - you don't just build, you shape direction\nPython, PyTorch (or equivalent), distributed training, ML pipeline orchestration\nNice to Have:\nExperience in FinCrime, fraud detection, AML, or regulated financial services\nExperience with graph-based methods (GNNs, entity resolution, link analysis) in production\nFoundation model fine-tuning or LLM evaluation experience\nExperience establishing modern ML practices in organisations scaling their ML capabilities\nInterested? Find out more:\nHow we work - a practical guide\n\nDEI @ Wise\n\nWise Tech Stack (2025 update)\n\nSee what it's like to work at Wise London!\n\nOur Engineering career map\n\nWise Engineering -https://medium.com/wise-engineering\n\nWhat do we offer:\nStarting salary: £145,000 - £182,000 + RSUs\n\nWise Benefits\n\n#LI-AB3 #LI-Hybrid\n For everyone, everywhere. We're people building money without borders - without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.\nWe're proud to have a truly international team, and we celebrate our differences.\nInclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.\nIf you want to find out more about what it's like to work at Wise visit Wise.Jobs.\nKeep up to date with life at Wise by following us on LinkedIn and Instagram.","description_format":"text","description_chars":4874,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United Kingdom","iso":"GB","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Financial Services"],"lifecycle":[{"event":"open","at":"2026-09-23T16:25:26Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":21,"reasons":["conf:0","win:early","comp:brand"],"computed_at":"2026-09-24T01:19:38Z"},"pay":{"stated_usd_annual":241800,"is_top_pay":false},"html_url":"https://alion.io/job/wise-staff-applied-ml-engineer-financial-crime-3","json_url":"https://alion.io/job/wise-staff-applied-ml-engineer-financial-crime-3.json","meta":{"generated_at":"2026-09-24T01:19:38Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}