Machine Learning Engineer – IV, Fraud

This is a fully remote position, open to applicants in India.

📋 Description

• Develop, manage, and take ownership of essential fraudulent computer vision (CV) models at Jumio, which influence the entire fraud product suite.

• Architect and implement machine learning, deep learning, and traditional CV techniques aimed at fraud detection.

• Conduct research to facilitate the application of advanced algorithms.

• Deploy models as AWS SageMaker endpoints or directly on devices.

• Keep abreast of the latest developments in machine learning, deep learning, and computer vision by reviewing academic literature and participating in industry conferences.

• Collaborate effectively with fellow engineers and product managers within an Agile development framework.

⛳️ Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related discipline.

• A minimum of 6+ years of professional experience in machine learning/deep learning, excluding time spent in academia (2 years with a Master’s degree).

• Required: experience training deep learning models for deepfakes, synthetic data, or generative AI data detection.

• Profound understanding of translating machine learning metrics into product objectives.

• Practical experience with PyTorch or TensorFlow, SKLearn, and in developing production-ready Python code.

• Proven experience in applying best practices for monitoring and maintaining machine learning models in production settings.

• Excellent communication and problem-solving abilities.

• Preferred Experience and Qualifications: Familiarity with working on large language models (LLMs) or vision language models (VLMs). Experience in image or video generation. Proficiency in cloud environments such as AWS or GCP. Experience working in global organizations across multiple time zones.

🏝️ Benefits

• Jumio fosters a diverse environment with individuals from various backgrounds, strengths, interests, and cultures. We encourage applications from candidates of all backgrounds and statuses.