Software Engineer II, Machine Learning

About Poshmark

Poshmark is a leading fashion resale marketplace powered by a vibrant, highly engaged community of buyers and sellers and real-time social experiences. Designed to make online selling fun, more social and easier than ever, Poshmark empowers its sellers to turn their closet into a thriving business and share their style with the world. Since its founding in 2011, Poshmark has grown its community to over 130 million users and generated over $10 billion in GMV, helping sellers realize billions in earnings, delighting buyers with deals and one-of-a-kind items, and building a more sustainable future for fashion. For more information, please visit , and for company news, visit newsroom.poshmark.com .

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