Staff ML Scientist / Engineer
Confirmed on the employer's own hiring board on Nov 14, 2026. First seen by Alion on Nov 13, 2026. Wise scores B on the Alion truth index.
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.
More about our mission and what we offer.
We’re looking for a strong technical contributor with a background in machine learning and systems engineering. Bonus point if they owned the roadmap of services/tools used by more than one internal team.
This is a highly autonomous person that identifies low-hanging fruit worth tackling to move us ever closer to the vision they create for the problem-space they own. They must be able to communicate with stakeholders and users, steer short-term deliveries without compromising milestones in the roadmap, set guidelines and standards.
They collaborate with Data Science Leads to align work that would otherwise be siloed. They review experiments and design documents to improve projects from the get-go.
This role is a unique opportunity to scale and advance the impact of Data Science in Servicing tribe - namely Fincrime, KYC and Customer Support squads. What you build will have a direct impact on Wise’s mission and millions of our customers.
About the Role:
Our team exists to enable hundreds of specialized risk ML models - their lifecycle, governance and interface with compliance - while removing as much of the maintenance toil from Data Scientist and product teams.
We split the work in two tracks: Labels and Models
- Labels: central platform for risk labels
- Models: libraries and services to experiment, eval, promote and track models
Historically Wise is a company of very autonomous and distributed teams, which means different implementations exist to prevent and mitigate risk vectors.
We’ve invested a lot into centralizing risk assessments and their inputs (features) and have strong adoption from FinCrime, Verification, Mitigation and Global Products teams.
We’re now expanding this investment to the next critical component in scaling Wise: more and better ML /AI while keeping auditors and regulators happy.
Here’s how you’ll be contributing:
- Software engineering: e.g. testing + CI/CD, monitoring/alerting + disaster recovery
- MLOps: Terraform and AWS infra, ML governance for hundreds of models
- Data Engineering: distributed processing at terabyte scale
- Science: drive experiments to prove value of new methodologies / algorithms applied to cross-team domains
You will drive high-priority projects from proof-of-concept to MVP, to service / tooling, estimate and measure impact.
A bit about you:
- Extensive experience with end-to-end distributed data systems, specially ML-centric ones;
- Previous experience as Data/ML Scientist/Engineer in large scale product;
- Excellent Software Engineering skills. Ability to work with Python and Java;
- Strong drive to solve problems for Data Scientists, high agency in complex scenarios;
- Ability to build incrementally, think from first principles and prioritize simpler solutions;
- Good communication skills with technical and non-technical audiences, to engage and manage project stakeholders;
- Strong problem solving skills with the ability to help refine problem statements and propose solutions taking effort-impact-scalability tradeoff into account;
Some skills that will make you stand out:
- Apache Spark, Iceberg, Kafka, dbt, GraphFrames, Ray
- Scikit-Learn, XGBoost, PyTorch, MLFlow, Graph ML
- AWS (S3, EMR, SageMaker, Lakeformation)
- Terraform, Docker, GitHub CI/CD
- A/B testing. ML explainability
Interested? Find out more:
What do we offer:
Wise's Restricted Stock Units (RSU's).
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.
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