🛠 Tech Stack

💼 About This Role

You'll design and build scalable ML infrastructure on AWS, enabling data scientists to develop, train, deploy, and monitor models reliably and efficiently. You'll work at the intersection of distributed systems and applied machine learning, creating robust foundations for ML teams.

🎯 What You'll Do

• Design, build, and operate scalable ML infrastructure on AWS
• Develop distributed training and batch processing systems using Ray
• Build and maintain infrastructure-as-code using Terraform
• Enhance observability, reliability, and cost visibility across ML workloads

📋 Requirements

• 5+ years in ML infrastructure, platform engineering, or production ML systems
• Knowledge of the machine learning model development lifecycle
• Experience with distributed systems, cloud computing, or large-scale data processing
• Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code

✨ Nice to Have

• Experience with Ray or distributed compute frameworks
• Experience building or operating a feature store
• Experience with streaming technologies like Kafka, Kinesis, Flink

🎁 Benefits & Perks

• 💰 Competitive salary plus bonus and equity package
• 🏖 Generous vacation policy and company-wide Chime Days
• 🏢 In-office perks including backup child, elder, and/or pet care
• 🚌 Subsidized commuter benefit
• 🏥 Medical, dental, vision, life, and disability benefits plus 401k match

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Phone Interview· 45 min
3. 3On-site Interviews· 4 hours