Staff Software Engineer, Ads ML Training Infrastructure

Ads ML Training Infra team owns the model training frameworks and infra that power model development and training across for all Ads models at Pinterest. The team is looking for a staff engineer with strong hands-on experience in large scale ML model training systems as well as capabilities in solving ambiguous technical problems and driving strategic efforts.

What you’ll do:

• Lead and drive efforts of building next-gen ML data and training systems that directly powers up to 100 production models to uplevel Pinterest monetization business.
• Optimize the ads ML training performance, efficiency and scalability by 10x from infra and model perspectives.
• Work with ML communities inside and outside the company to bring in new technologies to Pinterest to power new ML paradigms including generative AI and LLM. 
• Build strong partnership with other ML teams to accelerate ML development and training velocity and level up MLOps excellence. 
• Mentor and coach other engineers, guiding them through technical decisions and career development.

What we’re looking for:

• BS (or higher) degree in Computer Science, or a related field.
• ~8+ years of relevant industry experience in leading the design of large scale & production ML infra systems.
• Deep knowledge with at least one programming language (Java, C++, Python) and at least one big data framework (Spark, Ray).
• Good knowledge and experience in building deep learning models and familiarity with Pytorch or Tensorflow.
• Track of records in leading group projects, coaching peers and collaborating across functions and orgs.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement: 

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1-2 times per week and therefore needs to be in a commutable distance from one of the following offices Palo Alto, CA; San Francisco, CA; Seattle, WA; New York City, NY.

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