Senior Staff Software Engineer (Product ML Infrastructure)
Posted Updated
Description
- At Pinterest, AI isn’t just a feature, it’s a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that
- To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI
- 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
- 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
- Deep expertise in distributed ML systems, including production experience with both large-scale training and online inference
- Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience
- Experience with AI/ML modeling, recommender systems, Ads ranking, retrieval, feature platforms, or similarly demanding ML workloads
- Strong GPU performance knowledge, such as profiling, distributed execution, kernel and memory optimization, compilation, or quantization
- 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
- A track record of setting technical strategy and delivering company-wide infrastructure initiatives in ambiguous environments