Senior Machine Learning Engineer

🌔 About the Opportunity

In this role, you will design, build, and deploy machine learning systems to detect and prevent fraudulent activity across our crypto products, helping protect our customers and platform in real time.

You will join the Machine Learning team and collaborate closely with Product, Data, Engineering, and Operations to deliver high-impact ML solutions end-to-end.

Your work will directly improve fraud detection, strengthen customer trust, and influence key business metrics. This role is well suited to someone who enjoys owning problems end-to-end, thrives in fast-paced environments, and is motivated by solving complex, high-stakes challenges with real-world impact.

🚀 What you will do

• Build and deploy machine learning models to evaluate transactions and customer behaviour in real-time and batch environments

• Develop and iterate on fraud detection models through hypothesis-driven experimentation across multiple products

• Own the full ML lifecycle from feature engineering and model development through to deployment, monitoring, and continuous improvement

• Design and build features from large-scale datasets, including real-time feature generation

• Apply advanced modelling techniques (e.g. gradient boosting, deep learning) and take them to production

• Collaborate cross-functionally to ship models and ensure they deliver measurable impact

• Improve ML infrastructure through pipelines, automation, and internal tooling

• Monitor and evolve models to ensure reliability, performance, and robustness over time
  

🧑‍🚀 About You

• Must have experience and skills

• Proven track record of building, deploying, and maintaining machine learning models in production, ideally in fraud, payments, or other risk-focused domains

• Ability to frame problems, develop hypotheses, and iterate on models through structured experimentation

• Strong understanding of the end-to-end ML lifecycle, including monitoring and iteration in production

• Experience designing and operating real-time or high throughput data systems (e.g. think Apache Beam, Apache Spark, BigQuery, or similar)

• Strong problem-solving skills and ability to operate autonomously in a fast-moving environment

Nice to have experience

• Familiarity with modern ML infrastructure and orchestration tools (e.g. Kubeflow, Airflow, Vertex AI or similar)

• Experience with cloud platforms (GCP, AWS, or Azure) and distributed systems

• Background in time series data, adversarial modelling, or counterfactual analysis

Bonus points

• Experience with deep learning frameworks (e.g. PyTorch)

• Contributions to ML tooling, internal platforms, or open-source projects