Staff Machine Learning Engineer, Ads

Whatnot • Commerce • Marketplaces • 8 days ago
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Salary
$245k – $290k per year
Location
Hybrid (San Francisco, United States)
Seniority
Staff
Visa
H-1B filings in 12 months: 48 · for this role: 36 · green card filings: 8
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Aug 22, 2026. Whatnot scores C on the Alion truth index.

Overview
Company
Impact
Profile match

Whatnot

Whatnot is a US live shopping marketplace headquartered in San Francisco where sellers host livestreams to sell trading cards, collectibles, fashion, electronics, live plants and many other categories. Founded in 2019 and backed by investors including Andreessen Horowitz, Y Combinator and CapitalG, it operates in North America, Europe, Australia and Japan with teams in New York, Los Angeles, London, Dublin, Berlin, Krakow, Sydney and Tokyo. It hires software and application security engineers, product designers, sales managers for international markets, recruiters, communications managers and finance and tax staff.
whatnot.com • HQ: San Francisco, United States • Marketplaces • 1001-5000 employees • Est. 2019
Tech stack of Whatnot
Backed by Sequoia Capital, Y Combinator, Andreessen Horowitz

Join Whatnot, the largest live shopping platform in North America and Europe. As a Staff Machine Learning Engineer on the Ads team, you will shape the technical foundation of Whatnot's advertising marketplace, designing auction systems and building foundational services. You will work closely with product, machine learning, and infrastructure teams, and lead technical decisions for new auction and monetization capabilities.

Missions

  • Architect and lead the implementation of high-throughput, low-latency backend systems that power ad auctions, bidding, pacing, ranking, and delivery.
  • Design auction mechanisms that balance seller value, buyer experience, marketplace efficiency, and long-term ecosystem health.
  • Build foundational services for budget management, allocation, forecasting, experimentation, attribution, and measurement.
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