Machine Learning Engineer II, LLM Applied Science

Pinterest • San Francisco, CA

12h • Hybrid

About The Position

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.
As Pinterest Labs, you'll work on tackling new challenges in large language models (LLMs) and generative AI along with a world-class team of research scientists, and machine learning engineers. You'll conduct research that can be applied across Pinterest engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: LLM training and adaptation, instruction tuning and alignment, retrieval-augmented generation (RAG), multimodal foundation models, personalization and user modeling with LLMs, LLM evaluation and benchmarking, efficient traininging/inference, safety alignment, and inclusive AI.

Requirements

• MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field
• 1-2 years of internship or professional experience
• Strong foundation in modern deep learning for NLP (transformers, representation learning, scaling law)
• Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)
• Experience in research and in solving analytical problems
• Cross-functional collaborator and strong communicator
• Comfortable solving ambiguous problems and adapting to a dynamic environment

Responsibilities

• Contribute to cutting-edge research in LLMs and generative AI that can be applied to Pinterest problems
• Collect, analyze, and synthesize findings from data, translate research insights into practical, scalable solutions
• Curate and generate training data with strong quality controls
• Build reliable evaluation strategies for LLM systems (offline metrics, human evaluation, redteaming, robustness & safety)
• Write clean, efficient, and sustainable code, collaborate closely with engineering partners to land research into real systems.
• Develop LLM powered methods to solve modeling and ranking problems across growth, discovery, ads and search
• Explore and productionalize techniques such as instruction tuning, preference optimization, RAG / tool-calling, and prompt / model optimization.
• Scope and independently solve moderately complex problems

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1,001-5,000 employees