Role Purpose: The Machine Learning Engineer IV will play a critical role in advancing Jumio's Fraud team's mission to develop and enhance state-of-the-art solutions for fraud detection for ID verification purposes. This role is essential for ensuring the highest standards of security and user verification through the application of advanced machine learning and deep learning techniques, ultimately contributing to Jumio's leadership in the online identity verification, eKYC, and AML solutions market
Role Value: As a Machine Learning Engineer IV at Jumio, you will have the opportunity to significantly impact the security and user experience of our ID verification solutions. Your expertise in deep learning and computer vision will drive the development of innovative algorithms that keep Jumio at the forefront of the industry. By deploying and maintaining these models in production, you will ensure the robustness and reliability of our solutions, supporting our clients across diverse industries such as Financial Services, Travel, Sharing Economy, Fintech, and Gaming. Your contributions will be pivotal in maintaining Jumio's reputation as the leading provider of online identity verification solutions, helping to meet the growing demand for secure and seamless user verification globally.
Example Responsibilities
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- Develop, maintain, and own key fraudulent CV models of Jumio, which shapes the whole fraud product offering of Jumio.
- Design and implement machine learning, deep learning, classical CV focused on fraud detection.
- Research to support the deployment of the advanced algorithms.
- Deploy models as AWS SageMaker endpoints or directly onto devices.
- Stay updated with the latest advancements in machine learning, deep learning, and computer vision by engaging with academic papers and attending industry conferences.
- Work collaboratively with other engineers and product managers in an Agile development environment.
Experience and Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
- A minimum of 6+ years of commercial experience in the field of machine learning/deep learning, not including time spent studying (2 years with Master’s).
- Must have: experience with training of DL models for deepfakes, synthetic data, or gen AI data detection.
- Deep understanding of how to convert ML metrics to product goals.
- Hands-on experience with PyTorch or TensorFlow, SKLearn, and in developing production-grade Python code.
- Demonstrated experience in implementing best practices for monitoring and maintaining ML models in production environments.
- Strong communication and problem-solving skills.
Great to have Experience and Qualifications
- Experience in working with LLM’s or VLM’s.
- Work with image or video generation.
- Experience with cloud environments - AWS / GCP.
- Experience in working in global organizations across several timezones
Jumio Values:
IDEAL: Integrity, Diversity, Empowerment, Accountability, Leading Innovation
Equal Opportunities:
Jumio is a collaboration of people with different ideas, strengths, interests and cultures. We welcome applications and colleagues from all backgrounds and of all statuses.
About Jumio:
Jumio is a B2B technology company dedicated to eradicating online identity fraud, money laundering and other financial crimes to help make the internet safer. We leverage AI, biometrics, machine learning, liveness detection and automation to create solutions that are trusted by leading brands worldwide and respected by industry thought leaders.
Jumio is the leading provider of online identity verification, eKYC and AML solutions. With a global footprint, we’re expanding the team to meet strong client demand across a range of industries including Financial Services, Travel, Sharing Economy, Fintech, Gaming, and others.
Applicant Data Privacy
We will only use your personal information in connection with Jumio’s application, recruitment, and hiring processes, as described in Jumio’s Applicant Privacy Notice. If you have any questions or comments, please send an email to [email protected].
