Sr. Staff Software Engineer, Product ML Infrastructure
About this role
Engineers who thrive on setting technical direction for massive-scale machine learning systems will find a natural home in this role. The Product ML Infrastructure team at Pinterest builds unified data, training, feature, and serving infrastructure that powers critical products and advertising systems across the platform.
Working within this group, the individual will shape the technical vision and roadmap for model training and serving, with a particular emphasis on GPU efficiency and large-scale ranking platforms. Daily responsibilities include architecting distributed training and inference pipelines, improving hardware utilization, and collaborating closely with Ads and Core AI teams to safely productionize models at scale.
The position exists to drive company-wide infrastructure initiatives and mentor senior engineering talent. Ideal candidates bring deep expertise in distributed machine learning systems, strong systems programming skills, and a proven ability to navigate ambiguous technical landscapes.
What you will do
- Set the technical vision and roadmap for model training and serving across the Product ML Infrastructure team, creating reusable interfaces to data and feature platforms.
- Lead architectures for distributed training, fine-tuning, distillation, evaluation, and high-scale CPU and GPU inference.
- Improve performance efficiency across data loading, distributed execution, GPU kernels and memory, compilation, quantization, scheduling, and capacity.
- Build reliable and observable platforms that maintain strong quality guarantees alongside training and serving consistency.
- Partner with Ads and Core AI and machine learning teams to productionize features and models safely at platform scale.
- Drive cross-organizational architecture decisions, migrations, and operational standards while mentoring senior engineers.
- Use AI-assisted development tools to accelerate prototyping, performance diagnosis, and validation while maintaining data safeguards.
What you need
- Demonstrated track record of setting technical strategy and delivering company-wide infrastructure initiatives in ambiguous environments.
- Deep expertise in distributed machine learning systems, including production experience with both large-scale training and online inference.
- Strong GPU performance knowledge covering profiling, distributed execution, kernel and memory optimization, compilation, or quantization.
- Practical background with AI and machine learning modeling, recommender systems, Ads ranking, retrieval, or feature platforms.
- Strong systems programming and design skills utilizing C++, Java, or Python.
- High ownership and sound judgment regarding reliability, security, cost, and operational excellence.
- Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent practical experience.
Skills and tech stack
Questions candidates ask
What is the working model and remote policy for this role?
This role follows the PinFlex working model and requires being in the office for in-person collaboration 1 to 2 times per quarter. Candidates therefore need to live within a commutable distance of either the Palo Alto, CA or San Francisco, CA office.
What is the salary range for this position?
The base salary range for this position is $245,402 to $429,454 USD for US-based applicants. The position is also eligible for equity, with final compensation determined by factors such as location, travel, relevant prior experience, and specific skills.
What does the team actually build?
The Product ML Infrastructure team enables fast, safe, and efficient delivery of AI and machine learning solutions across Ads and Core critical products. They build unified data, training, feature, and inference infrastructure.