Machine Learning Engineer II - LLM Applied Science

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. 

What you’ll do:

• 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 we’re looking for:

• 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

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/quarter and therefore can be situated anywhere in the country.

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