Tech Stack

Description

You will design and operate robust ML job execution frameworks, build and maintain internal API servers and developer tools for Kubernetes orchestration, architect and scale batch and streaming pipelines for ML training, and monitor data infrastructure using tools like Prometheus and Grafana. You'll collaborate with ML scientists to turn research into production features.

Requirements

• Bachelor's degree in CS, Engineering, or related field; Master's preferred
• 3+ years experience in ML platform engineering, MLOps, or data infrastructure with enterprise-grade ML systems
• Proficiency in Python, Java, or Go, and solid understanding of data structures and algorithms
• Experience in cross-functional collaboration and leading projects
• Proficient in using LLM-powered tools like GitHub Copilot or ChatGPT

Responsibilities

• Design and operate ML job execution frameworks for training, inference, and post-processing
• Build and maintain internal API servers and tools to orchestrate ML jobs on Kubernetes
• Architect, implement, and scale batch pipelines for ML training and evaluation (e.g., Spark)
• Design and monitor data infrastructure using PostgreSQL and observability tools (Prometheus, Grafana)
• Collaborate with ML scientists to turn research outputs into user-facing product features