🛠 Tech Stack

💼 About This Role

You'll build and maintain scalable offline ML infrastructure powering training data generation and distributed model training. You'll work closely with experienced engineers to bridge research and production. This role offers the chance to shape systems that impact company-wide AI decision-making.

🎯 What You'll Do

• Build and maintain data pipelines for training dataset generation
• Contribute to infrastructure supporting distributed training workflows
• Improve reproducibility through dataset validation and monitoring
• Optimize performance and efficiency across data processing systems

📋 Requirements

• PhD in Computer Science, Machine Learning, or related field
• Strong foundation in machine learning systems or distributed systems
• Python experience with data-intensive workloads
• Familiarity with PyTorch, TensorFlow, Ray, or Spark

✨ Nice to Have

• Experience with workflow orchestration systems like Airflow or Flyte
• Exposure to large-scale data platforms (data lakes, warehouses, streaming)
• Publications in ML systems or distributed systems

🎁 Benefits & Perks

• 🏥 Comprehensive health, life, and disability insurance
• 🚗 Commute subsidy
• 📈 Employee stock ownership
• 🏖️ Generous vacation and personal days
• 🧠 Mental health and wellbeing programs

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter phone screen· 30 min
2. 2Technical phone interview· 60 min
3. 3Onsite/virtual interviews· 3-4 hours