Offline ML Infra Engineer — Large-Scale Pipelines

Unity; Mountain View (CA); On-site; USD 92,000 - 138,000; Full time

Job summary

Unity seeks a Machine Learning Engineer for the Offline Infrastructure team. You will build data pipelines that generate training data and support distributed training workflows while helping scale ML platforms across batch and streaming data.

You will collaborate with researchers and engineers, working with PyTorch, Ray, Airflow, and similar tools to improve reliability, reproducibility, and performance as our ML platform grows.

Qualifications

• Bachelor's degree in Computer Science, Machine Learning, Systems, or a related field.
• Strong foundation in machine learning systems, distributed systems, or large-scale data processing.
• 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.
• Strong problem-solving skills and ability to translate research ideas into practical systems.

Responsibilities

• Build and maintain data pipelines that generate training datasets for ML models and experiments.
• Contribute to infrastructure supporting 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

Data pipelines

Problem solving

Research to production

Education

Bachelor's degree in CS or related field

Tools

PyTorch

TensorFlow

Ray

Spark

Airflow

Flyte

Job description

Unity seeks a Machine Learning Engineer for the Offline Infrastructure team. You will build data pipelines that generate training data and support distributed training workflows while helping scale ML platforms across batch and streaming data.

You will collaborate with researchers and engineers, working with PyTorch, Ray, Airflow, and similar tools to improve reliability, reproducibility, and performance as our ML platform grows.