about 4 hours ago
Base Salary
$200k - $235k/yr
Responsibilities
- Collaborate with product managers, data scientists, and software engineers to identify ML opportunities.
- Design, build, and productionize end-to-end Machine Learning pipelines for various use cases.
- Investigate emerging fraud patterns and develop ML-based detections.
- Write, review, and ship clean, testable code for model training and pipeline optimization.
- Work with large-scale structured and unstructured data to improve ML models.
- Participate in code reviews and cross-team collaborations to enhance ML engineering culture.
- Adapt models and systems to evolving fraud attack landscapes.
Requirements
- 5–10 years of industry experience in applied Machine Learning.
- Strong programming skills in Python and familiarity with Scala or Java.
- Solid understanding of Machine Learning best practices and algorithms.
- Experience with ML frameworks like TensorFlow or PyTorch.
- Experience in building end-to-end ML pipelines for batch and real-time systems.
- Exposure to architectural patterns of large-scale software applications.
- Experience with test-driven development and incremental delivery.
- Exposure to the Trust and Risk domain is a plus.
- A Bachelor’s, Master’s, or PhD in CS/ML or a related field.
Benefits
- Eligible for bonus, equity, benefits, and Employee Travel Credits.