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Senior Machine Learning Engineer
Job Description
🌍 Locations Supported
🌔 About the Opportunity
Our engineering discipline builds the technology that enables MoonPay to learn quickly and scale easily. We organize in small cross-functional squads of 4-6 engineers and an embedded Product Manager and Product Data Analyst. We currently have squads across Crypto / NFT / Payments / FinCrime / KYC / Core Product and others. For this role, the initial focus will be on working on our FinCrime products, while mastering other product areas to then spearhead ML and AI adoption in the company.
🚀 What you will do
· Build and integrate ML solutions for evaluating transactions and customers’ behaviour in real-time and offline environments
· Support multiple Crypto products with tailored ML solutions to prevent fraudulent activities
· Experiment with various modeling techniques on our customer data and see your solution through to production by partnering cross-functionally with product, data, operations, and engineering teams
· Analyze large datasets using SQL, Python and Apache Beam to surface features
· Help build AI-powered automation tools or pipelines and propose improvements across the company
· Own the full lifecycle of ML solutions: data ingestion / processing → feature engineering → model development → deployment → iteration
· Maintain and improve our existing codebase, expanding our internal ML libraries and pipelines
💻 What you will be working with
· Python
· ML feature engineering for fraud prevention
· Gradient boosting algorithms (e.g. XGBoost/CatBoost)
· PyTorch
· Polars, Pandas and Numpy
· BigQuery
· Apache Beam
· BigTable
· FastAPI
· Docker and Kubernetes
· Kubeflow and Airflow
· Vertex AI
· Redis
· Pydantic
· DataDog
· Github
🧑🚀 About You
· Experienced in Machine Learning at leading Fintech startups or fast growing tech companies
· Confident in developing, releasing and maintaining ML models in a fast paced, iterative environment
· Experienced with some of our tech stack, or are confident you can cross train and up skill quickly
· Real world experience working with time series data for classification, counterfactual reasoning and adversarial models
· Understand data structures, pipelines and big data processing for real-time consumption
· Experienced with Cloud Native applications such as Google Cloud or similar e.g. AWS, Azure
