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Machine Learning Engineer
Skills
APIComputer ScienceDevopsData CollectionData ScienceDatabasesEnglish
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Hands-on experience with applied machine learning using large datasets such as PyTorch or Scikit-learn
- Strong SQL skills and familiarity with relational and non-relational databases
- Experience with end-to-end ML systems including feature pipelines model deployment monitoring and iteration
- Excellent written and verbal communication skills in English
- Bachelor's or Master's degree in Computer Science Engineering or a related discipline
Nice to have
- Fraud domain knowledge
- Risk knowledge
- Cybersecurity background
- Software engineering background
- CI/CD familiarity
- Docker familiarity
- Kubernetes familiarity
- Browser API knowledge
- Frontier LLM familiarity
Day to day
- Design and build real-time data pipelines and backend services that process device and behavioral data at scale.
- Develop, deploy, and monitor fraud detection models so they perform reliably and efficiently in production.
- Transform raw data into robust production-ready features and collaborate with engineering teams to integrate ML systems seamlessly.