PI
Pinterest

Sr. Machine Learning Engineer, Applied Science

Ort: San Francisco, CA, US; Remote, US · Bereich: Data, AI & Analytics · Level: Senior · USD161,263 – USD332,012 / yearly · Veröffentlicht: 28. Juli 2026 · Geprüft: 3. Nov. 2026

Rollenübersicht & Beschreibung

This Senior Machine Learning Engineer role focuses on developing Pinterest Canvas, an internal text-to-image generative model, within the Pinterest Labs organization. The role involves prototyping new model architectures, working with large-scale visual-text datasets, conducting RLHF, and publishing research, requiring hands-on experience with diffusion models and computer vision.
Hiring Telemetrie · Expansions-Signal

Team-Wachstum & Marktbewegung von Pinterest beobachten

Polaris7 aggregiert offene Vakanzen, strategische Marktbewegungen und M&A-Signale in Echtzeit. Erhalte automatische Signal-Briefings zu Pinterest und vergleiche das Ökosystem mit direkten Wettbewerbern.

Quellenhinweis & Transparenz: Diese Position wurde direkt über das öffentlich zugängliche Karriereportal von Pinterest erfasst. Maßgeblich sind die Angaben auf der Original-Stellenausschreibung des Arbeitgebers. Polaris7 erhebt keine Vermittlungsgebühren und führt kein Bewerber-Screening durch.
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Pinterest

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Weitere Positionen bei Pinterest

3 weitere offene Stellen

Vor 1 Tag verifiziertSan Francisco, CA, US; Remote, CA, USRemoteMarketing & GrowthLead

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Veröffentlicht 3. Nov. 2026
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This Brand Partnerships Manager role leads the rollout of Pinterest's Advertiser Co-Marketing Brand Toolkit across France, Germany, and the Netherlands, working with the Global Sales Organisation to qualify and deliver co-marketing opportunities. The role involves creating market-ready guidance, training internal teams and agency partners, and ensuring brand standards are upheld while driving commercial revenue. Requires fluency in French and English.

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This Staff Machine Learning Engineer role focuses on advancing Pinterest's recommendation research through agentic AI, specifically the Reflex project. The engineer will build LLM agents that automate hypothesis generation, implementation, and experimentation, and contribute to generative retrieval with Semantic IDs. This is a hands-on technical leadership role requiring deep ML fundamentals and experience building production systems.

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