Opportunity at
WiseSenior ML Engineering Team Lead - Financial Crime
Wise is seeking a Senior ML Engineering Team Lead for its financial crime division based in London. This on-site, full-time position comes with an annual starting…
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About This Role
Wise is seeking a Senior ML Engineering Team Lead for its financial crime division based in London. This on-site, full-time position comes with an annual starting salary range of GBP 135,000 to GBP 175,000, plus RSUs. Wise defends millions of users and immense transaction volumes against fraud, money laundering, and financial crime using machine learning architectures operating at a global scale of 100K requests per minute under sub-50ms latency SLAs.…
Job Description
Wise is seeking a Senior ML Engineering Team Lead for its financial crime division based in London. This on-site, full-time position comes with an annual starting salary range of GBP 135,000 to GBP 175,000, plus RSUs. Wise defends millions of users and immense transaction volumes against fraud, money laundering, and financial crime using machine learning architectures operating at a global scale of 100K requests per minute under sub-50ms latency SLAs.
In this greenfield role, the chosen candidate will direct the Risk Modelling engineering pillar within the FinCrime department, which operates alongside the Feature Platform and Learning Loop pillars. Responsibilities include building a declarative model factory, creating the experimentation engine for traditional and modern architectures, establishing operational oversight infrastructure for hundreds of models, and recruiting a team of Applied ML and ML Systems Engineers.
This position is suited for an experienced technical leader with a history of designing ML lifecycle automation teams at high-growth companies, possessing system-level and mathematical proficiency to handle strict latency limits and severe financial risk.
Responsibilities
- Lead the Risk Modelling pillar within the FinCrime organisation
- Architect a declarative pipeline to scale the deployment and monitoring of hundreds of models
- Establish a reusable experimentation path from research to high-throughput production
- Build operational infrastructure for automated retraining, drift detection, and threshold management
- Recruit, mentor, and manage a team of ML engineers while establishing an engineering-first culture
- Determine build versus consume strategies through cross-platform partnerships
Requirements
- Prior experience leading or building an ML Engineering or model lifecycle automation team at a high-growth company
- System-level and mathematical depth to design architectures, review pipelines, and debug latency regressions
- Experience working in high-throughput environments with tight latency constraints and high financial stakes
- Demonstrated track record of hiring, developing, and managing senior engineers
- Ability to make architecture-level decisions autonomously amidst incomplete information
Qualifications
- Experience at a tier-1 fintech or payments company
- Practical background using graph-based methods such as GNNs and entity resolution in production
- Experience with foundation model fine-tuning or LLM evaluation
- Background in assisting organisations during transitions from classical ML to deep learning
Core Skills
Benefits
- Starting salary between GBP 135,000 and GBP 175,000 per year
- RSUs
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
What is the location and work arrangement for this role?
This full-time position is located in London and is on-site.
What is the salary range for the Senior ML Engineering Team Lead position?
The starting salary ranges from GBP 135,000 to GBP 175,000 per year, accompanied by RSUs.
What team will this role lead?
You will lead the Risk Modelling pillar within Wise's FinCrime organisation, managing a team of Applied ML Engineers and Senior ML Systems Engineers.
Are there nice-to-have qualifications mentioned in the posting?
Yes, helpful backgrounds include working at a tier-1 fintech or payments firm, experience with graph-based methods or foundation model fine-tuning, and assisting organisations transitioning from classical ML to deep learning.
Sample Interview Questions
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