Senior Machine Learning Engineer
Hybrid
Posted 10d ago
Remote Work Policy
On-site
Categories
Machine Learning Engineer
About the job
You will 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. You’ll work with systems engineers, product teams, hardware partners, and AI/ML engineers to bring models into production with low latency, strong reliability, and efficient resource use. This role combines applied ML, inference optimization, evaluation, and production engineering, with a focus on benchmarking models, improving serving performance, validating quality, and building tooling that helps Cloudflare and its customers ship AI applications at Internet scale.
Responsibilities
- Develop, optimize, and productionize machine learning models for Cloudflare’s serverless inference platform, focusing on performance, reliability, and model quality.
- Build benchmarking and evaluation frameworks to measure latency, throughput, cost efficiency, and model behavior across various model families.
- Improve inference performance through techniques like 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 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.
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.
- Experience with large-scale inference serving frameworks or runtimes.
- 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.