What you'll do:

📍Partner 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. 📍Contribute scalable solutions across various serving stacks at the machine learning service and application layers. 📍Build and help set direction for ML infrastructure, such as feature construction patterns, data and model monitoring, online & offline scoring systems, and model usage patterns. 📍Develop high quality communication devices such as dashboards, notebooks, documents, and presentations to convey insights across a broad audience. 📍Define and advance our technical approach to scalable machine learning.

You

📍Curious about who thrives at Whatnot? We’ve found that low ego, a growth mindset, and leaning into action and high impact goes a long way here. 📍As our next Software Engineer, Machine Learning 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. I📍ndustry experience with a track record of applying practical methods to solve real-world problems on consumer scale data. 📍Extensive experience with Python for data science and machine learning software development e.g. Flask, FastAPI, Docker. 📍Ability to work autonomously and lead initiatives across multiple product areas and communicate findings with leadership and product teams. 📍Experience with operational databases such as PostgreSQL, DynamoDB, Elasticsearch, Redis. 📍Proficiency and experience in applied statistical and machine learning fields e.g. Recommendations, Search, Fraud & Anomaly Detection, Experimentation and Causal Analysis 📍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. 📍Professionalism around collaborating in a remote working environment and well tested, reproducible work. 📍Exceptional documentation and communication skills.