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:
šPartner closely across the machine learning, platform, and product engineering teams to train models to solve product problems and productionize data science and machine learning artifacts. šContribute scalable solutions across various serving stacks at the machine learning service and application layers. šBuild and help set direction for ML infrastructure, such as feature construction patterns, data and model monitoring, online & offline scoring systems, and model usage patterns. šDevelop high quality communication devices such as dashboards, notebooks, documents, and presentations to convey insights across a broad audience. šDefine and advance our technical approach to scalable machine learning.
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, Machine Learning you should have š5+ years of experience, plus: šBachelorās degree in Computer Science, Statistics, Mathematics, Software Engineering, a related technical field, or equivalent work experience. Išndustry experience with a track record of applying practical methods to solve real-world problems on consumer scale data. šExtensive experience with Python for data science and machine learning software development e.g. Flask, FastAPI, Docker. šAbility to work autonomously and lead initiatives across multiple product areas and communicate findings with leadership and product teams. šExperience with operational databases such as PostgreSQL, DynamoDB, Elasticsearch, Redis. šProficiency and experience in applied statistical and machine learning fields e.g. Recommendations, Search, Fraud & Anomaly Detection, Experimentation and Causal Analysis 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. šProfessionalism around collaborating in a remote working environment and well tested, reproducible work. šExceptional documentation and communication skills.