Sr. Staff Software Engineer, Product ML Infrastructure
A senior technical leader on Pinterest's Product ML Infrastructure team, setting direction for large-scale model training and serving systems used by Ads and Core products. Day to day this means architecting distributed training, GPU-optimized inference, and feature/data infrastructure in C++, Java, or Python.
- Compensation
- $245.4K–$429.5K / yr
- Location
- Palo Alto, CA, US; San Francisco, CA, US
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- 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: 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
Full description
About Pinterest:
Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.
Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.
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.
Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.
Sr. Staff Software Engineer, Product ML Infrastructure
Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.
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.
What you’ll do:
- 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.