🛠 Tech Stack

💼 About This Role

You'll design and evolve the large-scale offline ML platform at Unity, building infrastructure for training data generation and distributed model training. You'll ensure pipelines are reliable, scalable, and performant for growing data and workloads.

🎯 What You'll Do

• Design and operate large-scale data pipelines for ML training datasets.
• Develop infrastructure for distributed training with Pytorch and Ray.
• Integrate ML pipelines with orchestration systems like Flyte or Airflow.
• Improve reproducibility, observability, and performance of ML pipelines.

📋 Requirements

• Experience with distributed computing frameworks like Ray, Spark, or 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.

✨ Nice to Have

• Familiarity with the Ray ecosystem (Ray Data, Ray Train).
• Experience with modern data infrastructure (data lakes, warehouses, streaming platforms).
• Proven ability to lead technical direction across teams.

🎁 Benefits & Perks

• 🏥 Comprehensive health, life, and disability insurance
• 🚗 Commute subsidy
• 📈 Employee stock ownership
• 🏖️ Generous vacation and personal days
• 🎓 Training and development programs

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3System Design Interview· 60 min
4. 4Hiring Manager Interview· 45 min
5. 5Offer· N/A

🚩 Heads Up

• No visa sponsorship available
• No relocation support