🛠 Tech Stack

💼 About This Role

You'll design and evolve the large-scale offline ML platform at Unity, building reliable infrastructure for generating training datasets and orchestrating ML workflows. Your work will directly impact how training datasets are prepared and delivered to distributed training systems, ensuring scalability and efficiency. You'll partner closely with ML engineers to enable large-scale experimentation and model iteration.

🎯 What You'll Do

• Design and operate large-scale data pipelines for training datasets
• Develop infrastructure for distributed training with PyTorch and Ray
• Integrate ML pipelines with workflow orchestration systems like Flyte
• Optimize performance and resource utilization across distributed compute systems

📋 Requirements

• Distributed computing frameworks like Ray, Spark, or Flink
• Infrastructure for training data generation or ML feature pipelines
• Deep experience designing production-grade data pipelines
• Strong Python skills and large-scale distributed workloads

✨ Nice to Have

• Familiarity with Ray ecosystem (Ray Data, Ray Train)
• Experience with modern data infrastructure (data lakes, warehouses)

🎁 Benefits & Perks

• 🏖️ Comprehensive health, life, and disability insurance
• 🏖️ Commute subsidy
• 🏖️ Employee stock ownership
• 🏖️ Generous vacation and personal days
• 🏖️ Support for new parents

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min