Intern, Infrastructure

About the role

This summer, you'll join Unity Vector - the team that builds the offline ML platform powering insight, experimentation, attribution, and AI-driven decision-making across the company. Our systems operate at scale across batch and streaming data, supporting analytics, product intelligence, machine learning pipelines, and business operations. As data volume and complexity grow, our platform enables large-scale model training, feature generation, and experimentation workflows that power production ML systems. As an Infrastructure Intern on our Offline Infrastructure team, you'll work alongside experienced engineers and researchers on large-scale systems and apply research-driven thinking to real-world machine learning problems. You'll help build and evolve the infrastructure that powers training data generation, ML workflows, and distributed model training - contributing to systems that ensure our ML pipelines are reliable, scalable, and efficient. This internship offers the opportunity to bridge research and production by translating advanced ideas into systems that operate at scale.

Responsibilities

• Build and maintain data pipelines that generate training datasets for machine learning models and experimentation.
• Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray).
• Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) to support multi-stage ML pipelines.
• Improve reproducibility and reliability through dataset validation, monitoring, and testing.
• Partner with ML engineers to support experimentation and model iteration.
• Help optimize performance and efficiency across data processing and training systems.
• Contribute to the evolution of our offline ML platform architecture as it scales.

Requirements

• Currently pursuing a degree in Computer Science, Machine Learning, Systems, or a related field (Bachelor's or Master's)
• Foundation in machine learning systems, distributed systems, or large-scale data processing (through coursework, research, or projects).
• Experience with Python and working with data-intensive workloads.
• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark).
• Strong problem-solving skills and the ability to translate ideas into practical systems.
• Interest in building scalable, reliable infrastructure for machine learning.
• You might also have
• Exposure to data pipelines, model training workflows, or large datasets (academic or applied).
• Experience with workflow orchestration systems (Airflow, Flyte, etc.).
• Exposure to large-scale data platforms (data lakes, warehouses, streaming systems).
• Research or projects in ML systems, distributed systems, or related areas.
• Additional information
• Relocation support is not available for this position
• Work visa/immigration sponsorship is not available for this position

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Company Intel

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