Builds and optimizes distributed training infrastructure for large-scale multimodal AI models across thousands of GPUs. Requires deep expertise in PyTorch, CUDA, parallelization techniques, and GPU clusters.
(Preferred)
Base pay range: $187,500 – $395,000 per year
Develops and optimizes inference engines for multimodal AI models, integrating new architectures, building scheduling systems, and managing large-scale GPU deployments. Requires strong Python, model serving frameworks like PyTorch/vLLM, and Kubernetes expertise.
Builds foundational ML platform infrastructure including model serving pipelines, GPU scheduling systems, and CI/CD for large-scale multimodal AI models. Requires 5+ years in distributed systems with expertise in Python, Kubernetes, and AWS.
Design, train, and deploy large-scale ML recommendation systems and models that power personalization and discovery on Reddit. Requires a Master's degree and 3+ years building production ML systems.
Build and operate Chime's ML platform on AWS, including distributed training systems, feature stores, data pipelines, and CI/CD tooling. Partner with ML teams to improve reliability, observability, and developer experience for production models.