Senior Machine Learning Engineer

At Cloudflare, we're on a mission to help build a better Internet.

You'll help define how machine learning models run across Cloudflare's global network, from frontier open LLMs and real-time voice models to customer-deployed models served on heterogeneous GPUs and next-generation accelerators.

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.

Desirable Skills, Knowledge, and Experience

• 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.