Machine Learning Engineer (Internship)

Location: Mountain View, California, USA, or remote from Washington, USA

Base salary: $92,000–$152,000 per year, depending on geographic zone and experience.

Company

hirify.global develops a leading game engine and 3D technology platform used for games, XR, web experiences, and applications across industries including automotive, manufacturing, and healthcare.

What you will do

• Build and maintain data pipelines that generate training datasets for machine learning models and experimentation.
• Contribute to distributed training infrastructure using technologies such as PyTorch and Ray.
• Support multi-stage ML pipelines with workflow orchestration tools such as Airflow and Flyte.
• Improve reproducibility and reliability through dataset validation, monitoring, and testing.
• Partner with ML engineers on experimentation, model iteration, and research-to-production work.
• Optimize data processing and training systems as the offline ML platform 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 data-intensive workloads.
• Familiarity with ML frameworks such as PyTorch or TensorFlow and distributed systems such as Ray or Spark.
• Experience with data pipelines, model-training workflows, or large datasets.
• Professional English proficiency is required for regular verbal and written communication with worldwide colleagues and partners.

Nice to have

• Experience with Airflow, Flyte, or similar workflow orchestration systems.
• Exposure to data lakes, warehouses, or streaming systems.
• Publications or research in ML systems, distributed systems, or related areas.

Culture & Benefits

• Health, life, and disability insurance options.
• Employee stock ownership and retirement or pension plans.
• Generous vacation, personal days, parental leave, and family-care programs.
• Mental health and wellbeing support, employee resource groups, and global employee assistance.
• Training and development programs, volunteering support, office snacks, and commute subsidies.