Senior Machine Learning Engineer

Cloudflare Cloudflare · Enterprise · Austin, TX · Emerging Technology and Incubation

Senior Machine Learning Engineer to optimize and productionize ML models on Cloudflare's global network, focusing on low-latency inference, reliability, and efficient resource use. Responsibilities include building benchmarking and evaluation frameworks, improving inference performance through various optimization techniques, integrating models into distributed infrastructure, and driving model deployment workflows.

What you'd actually do

  1. Develop, optimize, and productionize machine learning models for Cloudflare’s serverless inference platform, with a focus on performance, reliability, and model quality.
  2. Build benchmarking and evaluation frameworks to measure latency, throughput, cost efficiency, and model behavior across LLMs, speech, vision, and other model families.
  3. Improve inference performance through quantization, batching, caching, model compilation, runtime tuning, and accelerator-aware optimization.
  4. Partner with systems engineers to integrate models into Cloudflare’s distributed inference infrastructure across a heterogeneous fleet of GPUs and next-generation accelerators.
  5. Drive improvements to model deployment workflows, including validation, rollout safety, observability, regression testing, and operational readiness.

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • JAX
  • inference optimization
  • quantization
  • batching
  • caching
  • compilation
  • serving runtime tuning
  • LLMs
  • speech models
  • vision models
  • embeddings
  • multimodal models
  • retrieval-augmented generation
  • deep learning architectures
  • GPUs
  • specialized accelerators
  • production ML concerns
  • evaluation
  • monitoring
  • model regressions
  • rollout safety
  • reliability
  • distributed systems
  • networking
  • serverless platforms

Nice to have

  • SGLang
  • vLLM
  • TensorRT-LLM
  • ONNX Runtime
  • Triton
  • llama.cpp
  • open source ML tooling
  • model serving frameworks
  • inference runtimes

What the JD emphasized

  • productionize machine learning models
  • low latency
  • strong reliability
  • efficient resource use
  • benchmarking models
  • improving serving performance
  • validating quality
  • building tooling
  • ship AI applications at Internet scale
  • production environments
  • inference optimization techniques
  • large-scale models
  • serving runtime tuning
  • optimizing models for GPUs or specialized accelerators
  • production ML concerns
  • evaluation
  • monitoring
  • model regressions
  • rollout safety
  • reliability

Other signals

  • productionizing ML models
  • inference optimization
  • serving performance
  • AI applications at Internet scale