Software Engineer
Machine Learning
Confirmed live in the last 24 hours
WhatNot

501-1,000 employees

Community marketplace platform
Company Overview
WhatNot’s mission is to enable anyone to turn their passion into a business & bring people together through commerce. The company has the fastest growing marketplace with their unique shopping experience for niche and passionate users.
Consumer Goods

Company Stage

Series D

Total Funding

$464.7M

Founded

2019

Headquarters

Marina del Rey, California

Growth & Insights
Headcount

6 month growth

22%

1 year growth

52%

2 year growth

222%
Locations
Remote in USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Apache Spark
AWS
Apache Kafka
Data Science
Docker
Elasticsearch
Flask
Postgres
Redis
Apache Flink
Python
Datadog
FastAPI
CategoriesNew
AI & Machine Learning
Software Engineering
Requirements
  • 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
  • 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
Responsibilities
  • 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