Machine Learning Engineer
Designs and builds ML systems for real-time fraud detection, including data pipelines, model deployment, and backend services in Go/Python. Requires 5+ years software engineering with applied ML experience on large datasets.
About the job
What you’ll be doing
- Build and optimize data pipelines and backend services to process device and behavioral data in real time.
- Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production.
- Turn raw data into production-ready features that feed our fraud detection systems.
- Collaborate with platform and backend engineers to integrate models seamlessly.
- Maintain high standards of security, privacy, and compliance.
- Champion best practices in testing, documentation, and observability.
What you’ll need
- 5+ years in software engineering, with strong backend experience (Go or Python).
- Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.).
- Strong SQL skills and familiarity with relational and non-relational databases.
- Experience with end-to-end ML systems: feature pipelines, model deployment, monitoring, and iteration.
- Excellent communication skills in English, both written and verbal.
- Bachelor's or Master's in Computer Science, Engineering, or a related discipline.
Bonus Points
- Domain knowledge in fraud, risk, or cybersecurity.
- Familiarity with CI/CD, Docker, Kubernetes and the modern devops framework.
- Understanding of modern browser APIs and high-entropy data collection techniques.
- Familiarity with leveraging frontier LLMs for automation.
Skills
Go, Python, PyTorch, scikit-learn, SQL, Docker, Kubernetes, CI/CD, LLMs
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