At a Glance
- Tasks: Build a cutting-edge ML platform to combat financial crime and enhance transaction security.
- Company: Join Wise, a global leader in money management and financial technology.
- Benefits: Competitive salary, RSUs, and a diverse, inclusive work environment.
- Other info: Dynamic team culture with excellent career growth opportunities.
- Why this job: Make a real impact on financial security while working with innovative technologies.
- Qualifications: Experience in ML infrastructure, strong software engineering skills, and a product mindset.
The predicted salary is between 111000 - 145000 £ per year.
Company Description
Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.
Whether 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.
As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.
Job Description
More about our mission and what we offer.
About The Role
- Wise is one of the fastest-growing global financial platforms, and as we scale, so does the sophistication of the ML systems protecting every transaction.
Our Risk ML team is building the model lifecycle platform that makes it possible to develop, deploy, and monitor ML models for financial crime detection – reliably, reproducibly, and at scale.
- We’re looking for a Senior ML Platform Engineer to build this platform from the ground up.
You'll design the infrastructure that turns model development from a bespoke, manual process into a scalable, standardised one – so our data and applied scientists can focus on improving detection rather than managing operations.
- How We Work
- Risk ML sits within Wise’s Fin Crime 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, building the platform layer that makes scaling our detection capabilities possible.
- You’ll work closely with data scientists, feature platform engineers (upstream infrastructure), and Wise's central ML platform team (shared foundations).
We value engineers who build for adoption – internal platforms succeed when teams want to use them.
What will you be working on?
- Designing and building the declarative training pipeline – standardised, config-driven model training that any data scientist can use without writing deployment code.
- Building model packaging and serving abstraction – a unified interface that handles multiple model types (classical ML, deep learning, emerging architectures) through a consistent API.
- Implementing the model evaluation framework – standardised metrics, reproducible comparison, and automated validation gates.
- Building model monitoring – drift detection, performance degradation alerts, automated retraining triggers, and full audit trails for regulatory compliance.
- Owning the integration layer with Wise's central ML infrastructure – aligning on boundaries so Fin Crime-specific lifecycle tooling builds cleanly on shared foundations.
- Maximising data science productivity – your platform's success is measured by how much time shifts from operational maintenance to improving detection performance.
What do you need?
- Experience building ML platform infrastructure in production – training pipelines, model serving, evaluation frameworks, or monitoring systems.
Infrastructure that other teams depend on, not individual model work.
- Strong software engineering fundamentals – you build reliable, well-tested, maintainable systems. Python, Kotlin/Java, SQL.
- Experience with ML orchestration (Airflow, Kubeflow, or equivalent), model registries (MLflow or similar), and container-based deployment.
- End-to-end understanding of the ML lifecycle – data ingestion through training, packaging, serving, and monitoring – and knowledge of where things break.
- A product mindset for internal tooling – you think about data scientists as users and build for adoption, not just functionality.
- Nice To Have
- Model serving at scale – latency optimisation, ONNX packaging, canary deployments for models.
- Experience in Fin Crime, fraud, AML, or regulated environments where audit trails and model governance are non-negotiable.
- Experience with model monitoring and drift detection systems in production.
- Track record of migrating teams from manual ML workflows to platform-based approaches.
Interested? Find out more
- How we work – a practical guide
- DEI @ Wise
- Wise Tech Stack (2025 update)
• See what it's like to work at Wise London!
- Our Engineering career map
- Wise Engineering –
- What Do We Offer
- Starting salary: £111,000 - £145,000 + RSUs
- Wise Benefits
- Additional Information
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.
We're proud to have a truly international team, and we celebrate our differences.
Inclusive 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.
If you want to find out more about what it's like to work at Wise visit Wise. Jobs.
Keep up to date with life at Wise by following us on Linked In and Instagram.
#J-18808-Ljbffr
Senior ML Platform Engineer II - Financial Crime in London employer: hackajob
At hackajob, we pride ourselves on being an exceptional employer that fosters a culture of innovation and inclusivity. Our diverse team thrives in a high-performance environment where your contributions directly impact our multi-asset platform's success. With ample opportunities for professional growth and a commitment to employee development, joining us means being part of a forward-thinking company that values your expertise and ambition.
StudySmarter Expert Advice🤫
We think this is how you could land Senior ML Platform Engineer II - Financial Crime in London
✨Tap into Campus Networks
If you're still in uni, don’t forget to engage with your campus's career services and attend finance-related events. Banks often do presentations and recruitment drives on campus, so put yourself out there and make use of these opportunities to show off your passion for the field.
✨Get Certified
Consider pursuing relevant certifications like the CFA or ACCA while you’re job hunting. They not only beef up your CV but also connect you with professional bodies which can lead to networking opportunities and even job openings in banking and financial services.
✨Connect on Professional Platforms
Join finance-focused groups on platforms like LinkedIn and engage in discussions. This can really help you stand out from the crowd, allowing potential employers to see your knowledge and interest in industry trends. Plus, you might stumble upon job postings shared exclusively within the group.
✨Apply Directly and Be Proactive
Don’t shy away from reaching out directly to firms like hackajob. Use their websites and apply through them, but also consider following up with a polite email to express your enthusiasm. Being proactive can make a huge difference in getting noticed in the competitive financial services sector.
We think you need these skills to ace Senior ML Platform Engineer II - Financial Crime in London
Some tips for your application 🫡
Show Off Your Numbers!:In the banking and financial services world, quantifiable achievements are key. Make sure your CV highlights your grades in relevant subjects, any financial certifications you hold, and specific projects where you've delivered measurable results. Employers love to see how your skills translate into real-world success.
Tailor Your Cover Letter to the Role:When applying for a full-time position, your cover letter should make a direct connection between your experience and the job description. Don't just state your enthusiasm for finance—dive into how your background in banking or financial analysis sets you apart. Let your passion shine through while being specific about what you can bring to hackajob.
Include Relevant Financial Software Experience:If you've worked with financial modelling tools or software like Excel, SAP, or specific analytical tools during your studies or internships, bring that up! Highlighting your proficiency can really make your application pop and show you're ready to hit the ground running in a full-time role.
Research and Reflect:Before hitting that 'apply' button on hackajob's website, do a little digging. Look up their recent projects, values, and culture. Reflecting their ethos in your application can make a huge difference and show you’re genuinely interested in being part of the team!
How to prepare for a job interview at hackajob
✨Brush Up on Financial Analysis Skills
Make sure you're well-versed in financial concepts and analytical techniques relevant to banking and financial services. Get comfortable with tools like Excel for modelling or financial forecasting, as technical questions in this area are common during interviews with hackajob.
✨Prepare for Case Studies
Expect to tackle case studies that demonstrate your problem-solving skills in real-world banking scenarios. Familiarise yourself with the types of problems you might face—think risk assessments or investment evaluations—and be ready to articulate your thought process clearly.
✨Show Your Passion for Finance
Since this is a full-time position, employers at hackajob will be keen to see your genuine interest in finance. Be prepared to discuss recent industry trends or news articles that excite you, showcasing your enthusiasm and engagement with the field.
✨Network with Industry Professionals
Before your interview, reach out to current or former hackajob employees on platforms like LinkedIn. They'll offer unique insights into the company's culture and the interview process, which can give us a delightful edge in showcasing a good fit for the team.
