ML Infrastructure Engineer - (early Career/internship)

Unity Unity · Enterprise · Canada · Remote · Templates

ML Engineer role focused on building and maintaining the offline ML platform infrastructure for data pipelines, distributed model training, and ML workflows. This role supports large-scale model training, feature generation, and experimentation, bridging research and production at scale.

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
  • data-intensive workloads
  • ML frameworks (e.g., PyTorch, TensorFlow)
  • distributed systems (e.g., Ray, Spark)
  • data pipelines
  • model training workflows
  • large datasets
  • scalable, reliable infrastructure for machine learning

Nice to have

  • workflow orchestration systems (Airflow, Flyte, etc.)
  • large-scale data platforms (data lakes, warehouses, streaming systems)
  • ML systems research
  • distributed systems research

What the JD emphasized

  • large-scale systems
  • real-world machine learning problems
  • large-scale model training
  • large datasets

Other signals

  • ML platform
  • large-scale model training
  • feature generation
  • experimentation workflows
  • production ML systems
  • ML pipelines