Discovery Feed Software Engineer
3 weeks ago
We're Whatnot, the largest livestream shopping platform in North America and Europe. We're building the future of ecommerce by combining community, shopping, and entertainment. Our remote co-located team operates out of hubs within the US, UK, Ireland, Poland, and Germany.
">Role OverviewWe're looking for a skilled Backend Software Engineer to join our Discovery Feed & Browse team. You'll partner with cross-functional teams to design scalable feed and browse solutions that meet critical business goals. The ideal candidate will support initiatives end-to-end and work closely with data and machine learning engineering teams.
Key Responsibilities- Ship product features to deliver high-quality Discovery experiences for users
- Build and maintain a scalable, stable, low-latency feed and browse experience
- Develop services and infrastructure to enable advanced recommendation systems for real-time, dynamic feeds
- Collaborate with machine learning, platform, and product engineering teams to utilize models and solve discovery problems
- Bachelor's degree in Computer Science, Statistics, Mathematics, Software Engineering, or a related field
- 5+ years of industry experience with a track record of applying practical methods to solve real-world problems on consumer-scale data
- Ability to work autonomously and lead initiatives across multiple product areas
- Experience with machine learning fields, such as Recommendations, Content Understanding, and Search
We provide a competitive salary range of $185,000-$245,000 per year, plus benefits and equity. Our compensation package includes flexible time off policy, health insurance options, work-from-home support, care benefits, retirement plans, and more.
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San Francisco, California, United States Whatnot Full timeAbout the RoleWe're seeking an experienced Software Engineer to design and build scalable feed and browse solutions for our Discovery Feed & Browse team. You'll partner across the company to deliver high-quality user experiences and work closely with data & machine learning engineering teams.
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