Machine Learning Engineer-Internship

Unity is hiring a Machine Learning Engineer Intern for its Offline Infrastructure team. The role contributes to infrastructure for training data generation, machine learning workflows, and distributed model training, while improving pipeline reliability, scalability, and efficiency.

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 (e.g., 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

Requirements:

• 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 (through research or projects)
• Experience with Python and working with data-intensive workloads
• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark)
• 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
• This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English
• Experience with workflow orchestration systems (Airflow, Flyte, etc.)
• Exposure to large-scale data platforms (data lakes, warehouses, streaming systems)
• Publications or research in ML systems, distributed systems, or related areas