Pinterest · 2 hours ago

Staff+ Machine Learning Engineer, Inclusive AI

San Francisco, CA

Full-time

Onsite

Lead/Staff

$205K/yr - $422K/yr

8+ years exp

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Comp. & Benefits

No H1B

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Responsibilities

Lead projects in the responsible AI frontier, to identify, avoid, and mitigate bias across a wide range of ML applications at Pinterest including generative AI.

Collaborate with other engineering teams (infra. user modeling, content understanding) to leverage their platforms and signals and work with them to collaborate on the adoption and evaluation of Inclusive AI and ML Fairness tooling across Pinterest.

Mentor junior engineers on the Inclusive AI team and across the company on the Inclusive AI / ML Fairness space.

Work with the team and senior leaders at the company to define and drive technical strategy in this area.

Qualification

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Machine LearningML FairnessLarge-scale SystemsProduction ExperienceTechnical StrategyTransformersBackend SystemsLeadershipMentoringCollaboration

Required

Experienced Engineer with production experience in ML and responsible technology (fairness, diversity, equity) deployments in production systems.

Familiarity with state of the art ML approaches, especially within the Search and RecSys space, such as transformer based architectures, n-tower architectures, LLMs, etc.

Hands-on experience building, deploying, and measuring ML Fairness at a very large scale. Successful candidates in this role need to be able to bridge state of the art approaches to real world impact.

Some experience in leadership roles where the candidate has been able to define a vision, strategy and plans, with concrete OKRs and execute on them to deliver solutions in production that have a positive impact for users.

8+ years working experience in the engineering teams that build large-scale ML-driven user-facing products.

3+ years experience leading cross-team engineering efforts.

Masters or PhD in Comp Sci or related fields.

Experience in working on backend and ML systems for large-scale user-facing products, and have a good understanding of how they all work.

Experience in closely collaborating with other engineering teams to ship ML technologies to improve fairness and reduce bias for recommendation, content understanding, and ranking systems at scale.