Machine Learning Engineer

About the role

This role advances fraud detection for ID verification through deep learning and computer vision. It operates within Jumio's Fraud team to develop state-of-the-art solutions. You will own the design and development of fraud detection models that directly define the capabilities of the fraud product offering. The position requires you to implement advanced algorithms using machine learning, deep learning, and classical computer vision techniques to solve real-world problems. You will guide research activities that translate into the deployment of cutting-edge algorithms within production environments. A core part of this role involves exposing models through scalable endpoints like AWS SageMaker or running them efficiently directly on devices. You must be able to translate complex machine learning metrics into clear product goals and business impact. Strong communication and problem-solving skills are essential to separate effective engineers from the rest in this domain.

Key facts

What you'll do

Fraud detection models are designed, developed, and owned to define the fraud product offering.

Advanced algorithms for fraud detection are implemented using machine learning, deep learning, and classical computer vision.

Research activities guide the deployment of advanced algorithms in production environments.

Models are exposed through AWS SageMaker endpoints or run directly on devices.

Hands-on experience with PyTorch or TensorFlow, SKLearn, and production-grade Python code is utilized daily.

Demonstrated practices for monitoring and maintaining machine learning models in production environments are followed rigorously.

The ability to translate machine learning metrics into product goals is necessary for success.

Strong communication and problem-solving skills are required to navigate complex technical challenges.

Experience with training deepfake, synthetic data, or generative AI data detection models is mandatory for this position.

You will work within an Agile environment alongside engineers and product managers to deliver high-quality software.

Requirements

A Bachelor's or Master's degree is mandatory in Computer Science, Data Science, Machine Learning, or a related field.

A minimum of 6+ years of commercial experience in machine learning or deep learning is required, with an alternative path of 2+ years when paired with a Master's degree.

Experience with training deepfake, synthetic data, or generative AI data detection models is mandatory.

The ability to translate machine learning metrics into product goals is necessary.

Hands-on experience with PyTorch or TensorFlow, SKLearn, and production-grade Python code is required.

Demonstrated practices for monitoring and maintaining machine learning models in production environments are required.

Strong communication and problem-solving skills are required.

Nice to have

Experience with large language models or vision-language models is noted.

Work with image or video generation is noted.

Cloud environment experience in AWS or GCP is noted.

Experience working in global organizations across multiple time zones is noted.

Practical notes

The position is remote based in India.

The role operates within an Agile environment alongside engineers and product managers.

Travel is not mentioned.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Machine learning engineers build and maintain models that power identity verification systems.

Deep learning and computer vision are central to modern fraud detection.

Production monitoring and metric interpretation are critical for reliable solutions.

Cloud platforms like AWS are common deployment environments.

Cross-functional collaboration is typical in regulated technology products.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels.

Some engineers move into management and lead teams of five to twenty people.

Others stay on the technical track.

Growth follows demonstrated impact, not tenure alone.

A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship.

Moving up usually requires owning outcomes end to end rather than completing assigned tickets.