below marketmost similar roles pay hereabove market
This role pays more than
90%
of similar roles. Most pay
$202,800–$254,750
— the blue band above.
At the midpoint, this role pays about
$290k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Pinterest
Pinterest is a visual discovery and inspiration platform where people find ideas for home, style, recipes, and more. It serves hundreds of millions of users worldwide through its image and video pinboard product.
Pinterest currently has
68 open roles
on FindRole.
Listed pay typically runs
$174,078–$339,078
across 61 roles with salary data.
The Staff Machine Learning Engineer, Content Visual AI joins the User and Content Foundations organization to drive the technical direction for foundational machine learning capabilities. This role involves leading high-impact scientific and engineering initiatives to build scalable content understanding systems, including reusable embeddings, semantic signals, and ML infrastructure. You will work across research, infrastructure, and engineering domains to transform advances in VLMs, LLMs, multimodal learning, and sequence modeling into product outcomes. Key responsibilities include setting multi-quarter strategies, managing distributed training of generative models, and establishing practices for data pipelines and inference. Required expertise includes visual representation learning, self-supervised learning, retrieval, and distillation. You will utilize tools like Spark, Hive, and MapReduce to solve complex problems in content understanding, search, and recommendation systems.
What does a Machine Learning Engineer earn in California?
Median $231150 from 187 postings across 27 companies.
Set the multi-quarter technical strategy for foundational ML capabilities in visual representation and multimodal content understanding.
Lead high-impact scientific initiatives from initial problem definition through research, experimentation, and productionization.
Build reusable embeddings, semantic signals, and ML infrastructure using VLMs, LLMs, and self-supervised learning.
Translate foundational ML capabilities into product outcomes by influencing cross-functional roadmaps and investments.
Establish rigorous practices for distributed training, data pipelines, inference, evaluation, and operational excellence.
Mentor engineers and scientists while aligning stakeholders on technical priorities, trade-offs, and execution plans.
What we're looking for
Minimum 7 years of industry experience.
Minimum 2 years of experience leading tech teams.
MS or PhD degree in computer science, machine learning, or equivalent industry experience.
Publications at top machine learning, multimodal, or data mining conferences or open-source ML experience.
Hands-on experience with large-scale distributed training of generative models like LLMs, VLMs, and sequence models.
Hands-on experience with distributed tooling for ML data pipelines such as Spark, Hive, and MapReduce.
Hands-on experience leading ambiguous ML and research efforts in building and improving GenAI models for content understanding, search, and recommendation systems.
Excellent cross-functional communication and collaboration skills.