Staff ML Engineer — Remote Multimodal Leader

Pinterest; San Francisco (CA); Hybrid; USD 234,000 - 390,000; Full time

Job summary

Pinterest is seeking an experienced Staff Machine Learning Engineer / Technical Lead to drive the team’s technical direction, build scalable content understanding systems, and impact core user and business outcomes.

You will lead ambiguous ML and research efforts across VLMs, LLMs, multimodal learning and sequence modeling, collaborating with research, machine learning, data, and engineering to translate capabilities into product impact.

Qualifications

• 7+ years of industry experience, including 2+ years leading teams.
• MS or PhD in computer science, machine learning, or equivalent.
• Publications at top ML conferences or open-source ML experience.
• Hands-on experience with large-scale distributed training of generative models (LLMs, VLMs).
• Experience with distributed tooling for ML data pipelines (Spark, Hive, MapReduce).
• Experience leading ambiguous ML/research efforts in GenAI for content understanding, search and recommendations.
• Technical leadership setting roadmaps spanning modeling, data pipelines and productionization.

Responsibilities

• Set technical direction and multi-quarter strategy for foundational ML capabilities.
• Lead high-impact scientific and technical initiatives from problem definition to adoption.
• Build reusable embeddings, representations, and ML infrastructure; advance VLMs/LLMs and sequence modeling.
• Collaborate with Product, Data Science, Applied Science and Engineering to translate capabilities into product outcomes.
• Mentor engineers and scientists; establish rigorous modeling, data pipelines, training, inference, and evaluation practices.

Skills

Technical leadership

Distributed training

GenAI models

Cross-functional collab

VLMs/LLMs

Education

MS/PhD in CS/ML

Tools

Spark

Hive

MapReduce

Job description

Pinterest is seeking an experienced Staff Machine Learning Engineer / Technical Lead to drive the team’s technical direction, build scalable content understanding systems, and impact core user and business outcomes.

You will lead ambiguous ML and research efforts across VLMs, LLMs, multimodal learning and sequence modeling, collaborating with research, machine learning, data, and engineering to translate capabilities into product impact.