Machine Learning Engineer, Offline Infrastructure (entry-level / New Grad Phd)

Unity Unity · Enterprise · Mountain View, CA · AI & Machine Learning

Machine Learning Engineer focused on building and maintaining the offline ML platform infrastructure for data pipelines, distributed training workflows, and ML pipelines at Unity. This role is for a recent PhD graduate interested in applying research to large-scale systems.

What you'd actually do

  1. Build and maintain data pipelines that generate training datasets for machine learning models and experimentation
  2. Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray)
  3. Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) to support multi-stage ML pipelines
  4. Improve reproducibility and reliability through dataset validation, monitoring, and testing
  5. Partner with ML engineers to support experimentation and model iteration

Skills

Required

  • Python
  • ML frameworks (e.g., PyTorch, TensorFlow)
  • Distributed systems (e.g., Ray, Spark)
  • Data pipelines
  • Model training workflows
  • Large datasets

Nice to have

  • Workflow orchestration systems (Airflow, Flyte, etc.)
  • Large-scale data platforms (data lakes, warehouses, streaming systems)
  • Publications or research in ML systems, distributed systems, or related areas

What the JD emphasized

  • PhD in Computer Science, Machine Learning, Systems, or a related field
  • Strong foundation in machine learning systems, distributed systems, or large-scale data processing (through research or projects)
  • Experience (academic or applied) with data pipelines, model training workflows, or large datasets
  • Strong problem-solving skills and ability to translate research ideas into practical systems
  • Interest in building scalable, reliable infrastructure for machine learning

Other signals

  • ML platform
  • large-scale systems
  • training data generation
  • distributed model training