Sr. Staff Software Engineer, Product ML Infrastructure

Pinterest•Palo Alto, CA

5h•$245,402 - $429,454•Hybrid

About The Position

Pinterest’s Product ML Infrastructure (PMLI) team enables fast, safe, and efficient delivery of AI/ML solutions across Ads and Core critical products. We build unified data, training, feature, and inference infrastructure; this role will set technical direction across model training and serving, with a focus on GPU efficiency and large-scale ranking systems.

Requirements

A track record of setting technical strategy and delivering company-wide infrastructure initiatives in ambiguous environments.
Deep expertise in distributed ML systems, including production experience with both large-scale training and online inference.
Strong GPU performance knowledge, such as profiling, distributed execution, kernel and memory optimization, compilation, or quantization.
Experience with AI/ML modeling, recommender systems, Ads ranking, retrieval, feature platforms, or similarly demanding ML workloads.
Strong systems programming and design skills in C++, Java, or Python.
High ownership and sound judgment in reliability, security, cost, and operational excellence.
Demonstrated ability to use AI to improve speed and critically evaluate AI-assisted work, with accountability for correctness, quality, and sensitive data.
Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience.

Responsibilities

Set the technical vision and roadmap for model training and serving across PMLI, with reusable interfaces to data and feature infrastructure.
Lead architectures for distributed training, fine-tuning, distillation, evaluation, and high-scale CPU/GPU inference.
Improve efficiency across data loading, distributed execution, GPU kernels and memory, compilation, quantization, scheduling, and capacity.
Build reliable, observable platforms with strong quality guarantees and training/serving consistency.
Partner with Ads and Core AI/ML teams to productionize features and models safely at Pinterest scale.
Drive cross-organizational architecture decisions, migrations, and operational standards; mentor senior engineers and raise the engineering bar.
Use AI-assisted development and analysis to accelerate prototyping, performance diagnosis, and validation while maintaining rigorous correctness and data safeguards.