ML Systems Engineer Intern (Offline Infra)

Unity; Mountain View (CA); On-site; USD 104,000 - 152,000; Full time

Benefits offered by this job

Comprehensive health insurance

Employee stock ownership

Generous vacation and personal days

Commuter subsidy

Mental Health and Wellbeing programs

Job summary

Unity is hiring a Machine Learning Engineer, Intern for its Offline Infrastructure team in Mountain View. The role focuses on building scalable data pipelines, supporting distributed training, and enabling ML workflows across batch and streaming data.

You will work closely with researchers and engineers to translate research ideas into reliable systems, ensuring quality, reproducibility, and performance of ML pipelines at scale.

Qualifications

• Bachelor’s degree in CS, ML, Systems or related field.
• Experience with Python and data-intensive workloads.
• Familiarity with ML frameworks (PyTorch, TensorFlow) and distributed systems (Ray, Spark).
• Experience with data pipelines, model training workflows, or large datasets.

Responsibilities

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

Skills

Python

Data pipelines

Distributed systems

ML systems

Research mindset

Big data

Collaboration

Education

Bachelor’s degree in Computer Science / Machine Learning / Systems

Tools

PyTorch

TensorFlow

Ray

Spark

Airflow

Job description

Unity is hiring a Machine Learning Engineer, Intern for its Offline Infrastructure team in Mountain View. The role focuses on building scalable data pipelines, supporting distributed training, and enabling ML workflows across batch and streaming data.

You will work closely with researchers and engineers to translate research ideas into reliable systems, ensuring quality, reproducibility, and performance of ML pipelines at scale.