Flip is hiring a
Machine Learning Infrastructure Engineer

Summary

The job is for a Machine Learning Infrastructure Engineer at Flip.shop, a social commerce company recently raised to a $1.05 billion valuation. The role involves designing and implementing scalable infrastructure for deploying, monitoring, and maintaining machine learning models, building tools for automation, leveraging cloud platforms, optimizing performance, collaborating with teams, ensuring security, and more.

Requirements

• 3+ years in infrastructure engineering, DevOps, or similar roles, with a focus on supporting machine learning workflows in production
• Strong proficiency in cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), CI/CD pipelines, and infrastructure-as-code tools (Terraform, Ansible). Experience with SageMaker is a bonus
• Experience working with machine learning frameworks (TensorFlow, PyTorch, or similar) and familiarity with MLOps practices
• Proven track record of optimizing infrastructure for performance, scalability, and reliability in production environments
• Strong teamwork skills, with the ability to partner with ML engineers and data scientists to streamline workflows
• Ability to communicate complex infrastructure solutions to technical and non-technical stakeholders
• Passion for solving infrastructure challenges that support real-time machine learning at scale

Responsibilities

• Design and implement scalable infrastructure for deploying, monitoring, and maintaining machine learning models in production environments
• Build tools to automate workflows for model training, testing, and deployment, ensuring that machine learning models can move quickly from development to production
• Leverage cloud platforms to create efficient, scalable systems for large-scale machine learning workloads
• Ensure the infrastructure supports high-performance model inference at scale, with a focus on minimizing latency and maximizing throughput
• Work closely with data scientists, machine learning engineers, and DevOps teams to create seamless integration between development and production environments
• Build robust monitoring systems to track model performance and infrastructure health, ensuring reliability and uptime of machine learning services
• Implement best practices in infrastructure security, data privacy, and compliance, particularly when handling sensitive user data