Software Engineer II – Machine Learning Platform

Software Engineer II – Machine Learning Platform

Requirements

• 5+ years of experience in MLOps / Platform Engineering / DevOps / Infrastructure
• Understanding of gold standard practices and best in class tooling for ML
• Experience building infrastructure for an ML platform and managing CPU and GPU compute
• Background in software development
• Experience with Infrastructure as Code using Terraform
• Understanding of CI/CD in building high-performing teams
• Experience with tools like Jenkins, CircleCI, Argo Workflows, and ArgoCD
• Passionate about observability and experience with tools such as Splunk, Nagios, Sensu, Datadog, New Relic
• Familiarity with containers and container orchestration, with experience in Docker and Kubernetes

Responsibilities

• Expand, mature, and optimize the ML platform built around cutting edge tooling like Ray, MLFlow, Argo, and Kubernetes
• Build and mature capabilities to support CPU / GPU clusters, model performance monitoring, drift detection, automated roll-outs, and improved developer experience
• Build, operate, and maintain a low-latency, high volume ML serving layer covering both online and batch inference use cases
• Orchestrate Kubernetes and ML training / inference infrastructure exposed as an ML platform
• Expose and manage environments, interfaces, and workflows to enable ML engineers to develop, build, and test ML models and services
• Close the latency gap on model inference to online, real-time model serving
• Develop automation workflows to improve team efficiency and ML stability
• Analyze and improve efficiency, scalability, and stability of various system resources
• Partner with other teams and business stakeholders to deliver business initiatives
• Help onboard new team members, provide mentorship and enable successful ramp up on your team’s code bases

Preferred Qualifications

No preferred qualifications provided.