ML Infrastructure Engineer – Early Career/Internship
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Job Description
Machine Learning Engineer focusing on data pipelines for ML models at Unity. Collaborating with teams to support experimentation and system reliability in the data infrastructure space.
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
- Nice to Have
- 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
Benefits:
- Comprehensive health, life, and disability insurance
- Commute subsidy
- Employee stock ownership
- Competitive retirement/pension plans
- Generous vacation and personal days
- Support for new parents through leave and family-care programs
- Office food snacks
- Mental Health and Wellbeing programs and support
- Employee Resource Groups
- Global Employee Assistance Program
- Training and development programs
- Volunteering and donation matching program
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