Software Engineer, Whatnot

• An ideal candidate will have experience building discovery backend and data systems at scale, with a knack for working closely with product engineers to solve immediate problems for our users while making right tradeoffs towards building a platform
• We’ll often prototype new ideas quickly, and then need to build them for extreme scale while coordinating closely with other teams - the ideal person for this role is an expert at both of these approaches
• As our next Software Engineer you should have 5+ years of experience, plus:
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Software Engineering, a related technical field, or equivalent work experience
• 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 and communicate findings with leadership and product teams. You can mentor others and prioritize building inclusive, supportive teams
• Experience in machine learning fields (e.g. Recommendations, Content Understanding and Search)
• Expert at designing and building scalable and maintainable backend systems
• Firm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana
• Familiarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Kafka, Flink/Spark, OpenSearch, ElasticSearch, Lucene, SOLR

Desirable

• We are looking for intellectually curious, highly motivated individuals to be foundational members in shaping the Discovery Platform
• You will play a critical role in defining the technical direction and designing a world-class scalable Discovery system that is scalable enough to keep pace with our business growth to power feed, browse, search and taxonomy systems while catering to the unique challenges of operating a marketplace for live shopping!
• Working in a highly cross functional role, you’ll make key decisions about how we integrate retrieval, ranking through machine learning, real-time and stream processing, content understanding, materialization into a highly personalized discovery experience that unlocks product iterations while providing strong reliability
• Build the services and infrastructure to enable advanced recommendation systems solutions for real-time, dynamic feeds
• Build a scalable, stable, low latency discovery experience
• Partner closely across the machine learning, platform, and product engineering teams to utilize models to solve discovery problems
• Contribute scalable solutions across various serving stacks at the feed, search, machine learning service, and Discovery application layers
• Define and advance our technical approach to scalable recommendation systems