Senior Machine Learning Engineer

Cloudflare is on a mission to help build a better Internet, running one of the world’s largest networks that powers millions of websites. The Senior Machine Learning Engineer will define how machine learning models run across Cloudflare’s global network, working with teams to bring models into production with low latency and strong reliability.

Responsibilities:

• Develop, optimize, and productionize machine learning models for Cloudflare’s serverless inference platform, with a focus on performance, reliability, and model quality
• Build benchmarking and evaluation frameworks to measure latency, throughput, cost efficiency, and model behavior across LLMs, speech, vision, and other model families
• Improve inference performance through quantization, batching, caching, model compilation, runtime tuning, and accelerator-aware optimization
• Partner with systems engineers to integrate models into Cloudflare’s distributed inference infrastructure across a heterogeneous fleet of GPUs and next-generation accelerators
• Drive improvements to model deployment workflows, including validation, rollout safety, observability, regression testing, and operational readiness
• Collaborate with product and engineering teams to translate customer requirements into scalable ML capabilities for Workers AI
• Mentor engineers, contribute to technical direction, and raise the quality bar for production ML engineering practices across the team

Requirements:

• Experience building, optimizing, and operating machine learning models in production environments
• Strong proficiency with Python and modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent
• Hands-on experience with inference optimization techniques for large-scale models, including quantization, batching, caching, compilation, and serving runtime tuning
• Experience with large-scale inference serving frameworks or runtimes such as SGLang, vLLM, TensorRT-LLM, ONNX Runtime, Triton, llama.cpp, or similar
• Familiarity with LLMs, speech models, vision models, embeddings, multimodal models, retrieval-augmented generation, or other modern deep learning architectures
• Experience optimizing models for GPUs or specialized accelerators
• Strong understanding of production ML concerns, including evaluation, monitoring, model regressions, rollout safety, and reliability
• Ability to work across ML and systems boundaries, including familiarity with distributed systems, networking, or serverless platforms
• Track record of leading complex technical projects and mentoring other engineers
• Experience contributing to open source ML tooling, model serving frameworks, or inference runtimes