Cloudflare
Senior Machine Learning Engineer
Hybrid +1 · Senior · Full-time · Posted Jul 2, 2026
- Location
- Hybrid
Austin, TX, United States - Experience
- Extensive Senior or Lead ML Engineering experience; 3+ years of dedicated ML Engineering experience is listed as nice-to-have.
- Education
- M.S. or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
- Visa sponsorship
- Offers may be conditioned on authorization to receive software or technology controlled under U.S. export laws without sponsorship for an export license.
About the job
Architect and operate a scalable, multi-tenant AI/ML platform and backend services supporting predictive models, generative AI, LLMs, and agent-based applications across Cloudflare.
What you'll do
- Design and evolve the unified AI/ML platform, including production AI agents, chatbots, multi-agent execution, and LLM orchestration.
- Build scalable backend services and data pipelines, and lead deployment, optimization, reliability, and long-term platform ownership.
- Provide technical leadership through design and security reviews, engineering standards, and evaluation of AI infrastructure tools.
What they ask for
- Extensive experience as a Senior or Lead ML Engineer architecting and operating production ML platforms, services, and distributed backends.
- Strong competency in traditional ML lifecycles and deep experience with generative AI patterns, including RAG, context engineering, fine-tuning, guardrailing, and agentic systems.
- Mastery of Python and experience with modern backend ecosystems.
- Builder’s mindset, comfort with ambiguity, and ownership of system reliability, costs, and model performance.
Nice to have
- 3+ years of dedicated ML Engineering experience in a large-scale enterprise environment.
- Experience with production GenAI and multi-agent systems, LLMOps, distributed infrastructure, MLOps, and cloud or data platforms.
Tools
Python, React, TypeScript, LangGraph, LangChain, Autogen, PyTorch, Cloudflare Workers
AI focus
Build and operate infrastructure for traditional ML, generative AI, LLM orchestration, production AI agents, chatbots, and multi-agent systems.
Model-written summary · the original posting is authoritative