ML Systems Engineer - Offline Infrastructure (PhD)

Unity3d

San Francisco (CA)

On-site

USD 112,700 - 169,100

Full time

14 days+
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Benefits offered by this job

Health insurance
Stock options
Retirement plan
Vacation & personal days
Parental leave
Commuter subsidy

Job summary

Unity Technologies, a leading real-time 3D development platform, is seeking a Machine Learning Engineer for its Offline Infrastructure team in San Francisco. You will help build and evolve data pipelines, support distributed model training, and translate research ideas into scalable systems.

Ideal candidates hold a PhD and have strong ML systems experience, Python proficiency, and familiarity with PyTorch/Ray/Spark.

Qualifications

  • PhD in Computer Science, ML, or related field.
  • Strong background in ML systems and distributed data processing.
  • Experience with Python for data-intensive workloads.
  • Familiarity with PyTorch, TensorFlow and distributed systems (Ray, Spark).
  • Experience with data pipelines and large datasets.
  • Ability to translate research ideas into production systems.

Responsibilities

  • Build and maintain data pipelines to generate training datasets and experiments.
  • Support distributed training workflows (e.g., PyTorch, Ray).
  • Work with workflow orchestration tools to support multi-stage ML pipelines.
  • Improve reproducibility and reliability via dataset validation, monitoring, and testing.
  • Partner with ML engineers to support experimentation and model iteration.
  • Help optimize performance across data processing and training systems.
  • Contribute to the evolution of offline ML platform architecture.

Skills

PhD in CS/ML
Distributed systems
Python
PyTorch
Data pipelines
Large-scale data

Education

PhD in CS/ML/Systems

Tools

PyTorch
TensorFlow
Ray
Spark

Job description

Unity Technologies, a leading real-time 3D development platform, is seeking a Machine Learning Engineer for its Offline Infrastructure team in San Francisco. You will help build and evolve data pipelines, support distributed model training, and translate research ideas into scalable systems.

Ideal candidates hold a PhD and have strong ML systems experience, Python proficiency, and familiarity with PyTorch/Ray/Spark.

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