ML Systems Engineer, Offline Infrastructure

Unity; San Francisco (CA); Hybrid; USD 92,000 - 138,000; Full time

Benefits offered by this job

Stock options

Health insurance

Retirement plans

Generous vacation and personal days

Job summary

Unity is seeking a Machine Learning Engineer for the Offline Infrastructure team in San Francisco. You will build data pipelines and ML workflows to generate training data and support experimentation at scale.

You will collaborate with engineers and researchers to translate research ideas into reliable, scalable systems using PyTorch, Ray, and Airflow, enhancing performance and reproducibility across ML pipelines.

Qualifications

• Bachelor’s degree in Computer Science, Machine Learning, Systems, or a related field.
• Experience with Python and data-intensive workloads.
• Familiarity with ML frameworks (PyTorch, TensorFlow) and distributed systems (Ray, Spark).

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 (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

Skills

Python

ML systems

Distributed systems

Large-scale data processing

Data pipelines

Model training workflows

Research to production

Education

Bachelor’s degree in Computer Science / ML / Systems

Tools

PyTorch

TensorFlow

Ray

Spark

Job description

Unity is seeking a Machine Learning Engineer for the Offline Infrastructure team in San Francisco. You will build data pipelines and ML workflows to generate training data and support experimentation at scale.

You will collaborate with engineers and researchers to translate research ideas into reliable, scalable systems using PyTorch, Ray, and Airflow, enhancing performance and reproducibility across ML pipelines.