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What is the difference between Internship Machine Learning Startup vs Data Science Intern?

AspectInternship Machine Learning StartupData Science Intern
Required CredentialsBasic programming, statistics, coursework in MLSimilar; often includes coursework in data analysis and statistics
Work EnvironmentFast-paced startup, collaborative teamsVaries; startups or corporate settings, collaborative
Industry UsageCommon in tech startups focusing on AI/ML productsWidespread across tech, finance, healthcare
Search & Comparison IntentInterested in ML-specific roles in startupsLooking for data analysis or data science internships

Internship Machine Learning Startup roles focus on applying ML techniques in startup environments, often requiring programming and statistical skills. Data Science Internships may encompass broader data analysis tasks across various industries. Both roles share similar credentials and work environments, but ML internships are more specialized in machine learning applications within startups.

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Internship Machine Learning Startup jobs near you
Infographic showing various Internship Machine Learning Startup job openings in Kansas as of June 2026, with employment types broken down into 2% Internship, 83% Full Time, 7% Part Time, 1% Temporary, 4% Contract, and 3% Nights. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution.

Research Engineer - Machine Learning & Robotics

Jumio Corporation

Lenexa, KS • On-site

Full-time

Re-posted 10 days ago

Job description

Job Summary:
Jumio Corporation is a B2B technology company dedicated to eradicating online identity fraud and financial crimes. They are seeking a Research Engineer to help build and scale systems for data collection, model development, and product improvement in the fields of machine learning and robotics.
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.
Qualifications:
Required:
• 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.
Preferred:
• 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.
Company:
Jumio helps organizations to know and trust their customers online. Founded in 2010, the company is headquartered in Sunnyvale, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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