Note: The job is a remote job and is open to candidates in USA. Cloudflare is a technology company focused on helping build a better Internet through its global network and Internet security services. The Senior Machine Learning Engineer will develop and productionize models for Cloudflare’s serverless inference platform, optimize inference across heterogeneous hardware, build evaluation and benchmarking tools, and improve production ML deployment workflows for AI applications at Internet scale.
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
Skills
- 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
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