ML Systems Intern: Build Scalable Training Pipelines

Unity Technologies SF; Mountain View (CA); Presencial; USD 40.000 - 60.000; A tiempo parcial

Descripción de la vacante

Unity Technologies SF in Mountain View, CA is seeking a Machine Learning Engineer, Intern for the Offline Infrastructure team. You will help build data pipelines and distributed training workflows, collaborating with ML engineers and researchers to scale ML pipelines.

This internship bridges research and production, translating ideas into scalable systems, with exposure to PyTorch, Ray, and Airflow, and opportunities to influence data generation and model iteration.

Formación

• Bachelor's degree in Computer Science, ML, Systems, or related field.
• Strong foundation in ML systems, distributed systems, or large-scale data processing.
• Experience with Python and data-intensive workloads.
• Familiarity with ML frameworks (PyTorch, TensorFlow) and/or distributed systems (Ray, Spark).

Responsabilidades

• Build and maintain data pipelines that generate training datasets for ML models and experiments.
• Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray).
• Work with workflow orchestration tools (Airflow, Flyte) to support multi-stage ML pipelines.
• Improve reproducibility and reliability through dataset validation, monitoring, and testing.

Conocimientos

Machine learning systems

Distributed systems

Python

Data pipelines

Problem solving

Educación

Bachelor's degree in CS/ML/Systems

Herramientas

PyTorch

TensorFlow

Ray

Spark

Airflow

Descripción del empleo

Unity Technologies SF in Mountain View, CA is seeking a Machine Learning Engineer, Intern for the Offline Infrastructure team. You will help build data pipelines and distributed training workflows, collaborating with ML engineers and researchers to scale ML pipelines.

This internship bridges research and production, translating ideas into scalable systems, with exposure to PyTorch, Ray, and Airflow, and opportunities to influence data generation and model iteration.