ML Systems Engineer Intern: Scale Data Pipelines & Training

Unity; Mountain View (CA); On-site; USD 34,000 - 48,000; Full time

Benefits offered by this job

Comprehensive health, life and/or disa

Employee stock ownership

Generous vacation and personal days

Office food and free snacks

Training and development programs

Commute subsidy

Job summary

Unity is seeking a Machine Learning Engineer Intern to join the Offline Infrastructure team. You’ll help build and evolve the infrastructure that powers training data generation, ML workflows, and distributed model training.

Working closely with experienced engineers and researchers, you’ll contribute to systems that ensure our ML pipelines are reliable, scalable, and efficient. You’ll focus on data pipelines, multi-stage ML workflows, and experimentation support, bridging research ideas with

Qualifications

• Bachelor's degree in CS, ML, systems or related field.
• Strong foundation in ML systems or large-scale data processing.
• Ability to translate research ideas into practical systems.
• Experience with data pipelines or model training workflows.
• Interest in scalable, reliable ML infrastructure.

Responsibilities

• Build data pipelines that generate training datasets for ML models.
• Contribute to infrastructure for distributed training workflows (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.
• Partner with ML engineers to support experimentation and model iteration.

Skills

ML systems

Distributed systems

Data processing

Problem solving

Research to production

Education

Bachelor's degree

Tools

Airflow

Flyte

PyTorch

TensorFlow

Ray

Spark

Job description

Unity is seeking a Machine Learning Engineer Intern to join the Offline Infrastructure team. You’ll help build and evolve the infrastructure that powers training data generation, ML workflows, and distributed model training.

Working closely with experienced engineers and researchers, you’ll contribute to systems that ensure our ML pipelines are reliable, scalable, and efficient. You’ll focus on data pipelines, multi-stage ML workflows, and experimentation support, bridging research ideas with