About the company
Whatnot is a livestream shopping platform and marketplace backed by Andreessen Horowitz, Y Combinator, and CapitalG. Weāre building the future of ecommerce, bringing together community, shopping and entertainment. We are committed to our values, and as a remote-first team, we operate out of hubs within the US, Canada, UK, and Germany today. Weāre innovating in the fast-paced world of live auctions in categories including sports, fashion, video games, and streetwear. The platform couples rigorous seller vetting with a focus on community to create a welcoming space for buyers and sellers to share their passions with others.
Job Summary
What you'll do:
šShip product features to deliver high-quality Discovery experiences for users šBuild and maintain a scalable, stable, low latency feed and browse experience šBuild the services and infrastructure to enable advanced recommendation systems solutions for real-time, dynamic feeds šPartner closely across the machine learning, platform, and product engineering teams to utilize models to solve discovery problems šContribute scalable solutions across various serving stacks at the feed, search, machine learning service, and Discovery application layers. šDefine and advance our technical approach to scalable recommendation systems.
You
šCurious about who thrives at Whatnot? Weāve found that low ego, a growth mindset, and leaning into action and high impact goes a long way here. šAs our next Software Engineer you should have 5+ years of experience, plus: šBachelorās degree in Computer Science, Statistics, Mathematics, Software Engineering, a related technical field, or equivalent work experience. šIndustry experience with a track record of applying practical methods to solve real-world problems on consumer scale data. šAbility to work autonomously and lead initiatives across multiple product areas and communicate findings with leadership and product teams. You can mentor others and prioritize building inclusive, supportive teams. šExperience in machine learning fields (e.g. Recommendations, Content Understanding and Search). šExpert at designing and building scalable and maintainable backend systems. šFirm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana šFamiliarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Kafka, Flink/Spark.