🛠 Tech Stack

💼 About This Role

You'll design and evolve the large-scale offline ML platform at Unity, building reliable infrastructure for training dataset generation and distributed model training. You'll partner with ML engineers to ensure pipelines efficiently handle growing data volumes and complex workloads.

🎯 What You'll Do

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

📋 Requirements

• Experience with distributed computing frameworks such as Ray, Spark, Flink
• Experience building infrastructure for training data generation or ML feature pipelines
• Deep experience designing and operating production-grade data pipelines
• Strong programming skills in Python and large-scale distributed workloads

🎁 Benefits & Perks

• 🏖️ Generous vacation and personal days
• 💻 Employee stock ownership
• 🏥 Comprehensive health, life, and disability insurance
• 🧠 Mental Health and Wellbeing programs
• 📚 Training and development programs

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Onsite Interviews· 4 hours