Role Purpose

Jumio is looking for a Research Engineer with a foundation in machine learning, robotics, and data infrastructure to help build and scale the systems used for data collection, model development, and product improvement.

This role sits at the intersection of robotics, computer vision, and applied machine learning. You will work hands-on with robotic systems, ROS/ROS2-based modules, mobile data collection workflows, and ML pipelines that support training, evaluation, and production model performance. This is a strong opportunity for a new graduate or early-career engineer who wants to build practical systems that directly improve real-world AI products.

Role Value

High-quality data and reliable model evaluation infrastructure are critical to improving Jumio’s machine learning and computer vision capabilities. This role helps ensure that data collected from robotic systems and mobile applications is usable, scalable, and connected to the broader model development lifecycle.

The Research Engineer will support both the robotics/data collection environment and the ML development workflow, helping the team move faster, improve model quality, and better understand model performance in production.

Example Responsibilities

• Build and integrate ROS/ROS2-based modules to support robotic navigation, manipulation, and data collection workflows.
• Replicate and integrate mobile and web UI environments into robotic testing and data collection systems.
• Build, maintain, and improve training and test datasets collected through robotic manipulators and in-house iOS and Android applications.
• Mine, query, and analyze data from internal databases to create features, identify trends, and generate insights that improve product and model development.
• Develop tools and processes to monitor data quality, model performance, and model accuracy in production environments.
• Implement end-to-end machine learning workflows, including data preparation, model training, testing, evaluation, and deployment support.
• Write clean, modular, well-documented C++ and Python code that can be maintained and extended by other engineers.
• Collaborate cross-functionally with machine learning, engineering, product, and research teams to improve data collection, model development, and system performance.

Required Experience

• Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field.
• 1–2 years of relevant industry, internship, or research experience in machine learning, robotics, computer vision, or related technical areas.
• Hands-on experience with ROS and/or ROS2, including building or integrating modules for robot navigation, manipulation, simulation, or data collection.
• Strong foundation in machine learning fundamentals, with experience implementing models in Python using frameworks such as PyTorch, TensorFlow, scikit-learn, or similar.
• Experience working with databases, writing queries, and building or maintaining data pipelines for training, testing, or evaluation.
• Strong programming skills in Python and C++, with an emphasis on clean, reliable, well-documented code.
• Ability to work hands-on with physical hardware, debug system behavior, and translate research or prototype work into scalable engineering solutions.

Nice to Have

• Experience with robotic manipulators, mobile robot platforms, or lab-based robotic systems.
• Familiarity with iOS and/or Android development, especially for hardware-integrated data collection applications.
• Experience with data collection pipelines for computer vision, biometric systems, identity verification, or similar applied AI domains.
• Exposure to production ML observability, model monitoring, drift detection, or data quality monitoring tools.
• Familiarity with cloud platforms such as AWS, including S3, EC2, SageMaker, or similar tools for storage, compute, and model deployment.
• Experience working in cross-functional environments with machine learning engineers, software engineers, researchers, and product teams.