Lead ML Engineer & AI Platform Architect

CloudFlare; San Francisco (CA); Hybrid; USD 190,000 - 260,000; Full time

Benefits offered by this job

Health Insurance

Dental Insurance

Vision Insurance

401(k) Plan

Flexible PTO

Job summary

Cloudflare is seeking a Lead Machine Learning Engineer in Austin, TX to architect and drive the next generation of a unified AI/ML platform. You will design infrastructure powering traditional models, generative AI, LLMs, and autonomous agent frameworks, owning end-to-end technical strategy and deployment across teams.

You will lead multi-tenant backend services, data pipelines, and model inference, ensuring reliability, performance, and cost-efficiency, while mentoring engineers and shaping the

Qualifications

• Advanced degree or higher in a quantitative field.
• Proven track record architecting production ML platforms.
• Experience with AI/LLM patterns and agent systems.
• Strong Python and backend development skills.
• Familiarity with web tech (React/TypeScript) is valued.

Responsibilities

• Architect and evolve a scalable AI/ML platform unifying traditional ML and Generative AI orchestration.
• Design robust production-grade AI Agents and multi-agent systems with memory architectures.
• Build high-throughput backend services and data pipelines for AI-driven apps.
• Provide technical leadership, security reviews, and design reviews across teams.
• Evaluate modern AI infra tools and optimize embeddings and vector databases.

Skills

Senior ML Engineer

ML platform design

Python

LLM/Agentic systems

System reliability

Education

MS or PhD in CS/Stats/Math

Tools

Docker

Kubernetes

Terraform

BigQuery

Postgres

Airflow

Argo Workflows

LangGraph/LangChain

Job description

Cloudflare is seeking a Lead Machine Learning Engineer in Austin, TX to architect and drive the next generation of a unified AI/ML platform. You will design infrastructure powering traditional models, generative AI, LLMs, and autonomous agent frameworks, owning end-to-end technical strategy and deployment across teams.

You will lead multi-tenant backend services, data pipelines, and model inference, ensuring reliability, performance, and cost-efficiency, while mentoring engineers and shaping the