layiq
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Job opportunity
Software Engineer II, Machine Learning
Poshmark
Redwood City, California, United States
Source: Poshmark careers · View original posting
Employer job description
From Poshmark's posting. “We” and “our” refer to the employer.
About Poshmark
Poshmark is the leading fashion marketplace where style comes alive through discovery, self-expression, and human connection.
Powered by a vibrant community of 165 million members, Poshmark brings real people and taste to shopping through a social experience shaped by shared discovery. Buying and selling fashion feels simple, joyful, and personal, while every item tells its own story. Poshmark empowers sellers to grow meaningful businesses, keeps fashion in circulation longer, and gives shoppers access to unique and trusted finds, from everyday pieces to one-of-a-kind vintage and luxury.
Our AI / Machine Learning team is a central player in the Poshmark organization. Our mission is to build a world-class machine learning platform to bring value out of data for us and for our customers. Our goal is to democratize data science and machine learning, support exploding business, and use machine learning to drive value across the chain (Search, personalization, fraud detection, catalog digitization to name a few).
The Machine Learning Engineering team at Poshmark is looking for an experienced machine learning engineer to take care of Poshmark’s requirement to take machine learning models with varying requirements to production ..
Responsibilities:
- Build tools and infrastructure to democratize ML
- Productionizing ML models in collaboration with the Data Science and other Engineering teams.
- Maintain and support existing platforms and evolve to newer technology stacks and architectures.
- Desired Skills & Experience: 2-3 years of relevant software engineering experience with data intensive applications
- Good understanding of data science concepts and machine learning lifecycle
- Good understanding of spark and sql and prior experience with writing backend apis
- Flexible and open to trying out newer technologies and adopting them as and when needed.
- Technologies we use: Flask, Docker, Kubernetes
- Redis, Redshift, MongoDB, Kafka, RabbitMQ, Kinesis sklearn, pyTorch, Tensorflow, Spark
- Mlflow, Sagemaker, Kibana, Airflow