Machine Learning Engineer

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.

Job Description

The 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 businesses, and use machine learning to drive value across the chain (Search, personalization, fraud detection, and catalog digitization to name a few).

Responsibilities:

• Manage the entire ML lifecycle from data collection to deployment and monitoring.

• Collaborate across teams such as DS, QA, Infra, and other engineering teams to productionize ML models.

• Write and optimize code for production environments, ensuring the robustness and reliability of ML services at scale.

• Manage and support current solutions while evolving them to incorporate newer technologies.

• Strong written, verbal, and presentation skills, with the ability to convey complex concepts in a clear and simple manner.

• Stay updated with the latest developments in data science and machine learning.

Requirements:

• 2+ years of experience applying Machine Learning to concrete problems at large scale.

• Bachelors or Masters in Computer Science, Statistics, or related field.

• Strong CS fundamentals. Should be able to write algorithms with ease.

• Solid understanding of Data Science and ML fundamentals - Regression, Classification, Tree-based approach, Neural network, and sequence-based models.

• Working experience with at least one ML model: LLMs, GNN, Deep Learning, Logistic Regression, Gradient Boosting trees, etc.

• Should have an excellent understanding of the ML lifecycle.

• Good understanding of system architecture. Have knowledge of big data technologies - streaming architecture, data pipelines, etc.

• Experience with Python, SQL, Java, or Scala.

Good to have:

Big data systems - Spark, EMR, S3 AirFlow.

Production experience with LLMs and prompt engineering.

Experience with Flask, FastAPI, RabbitMQ, Embeddings, and Vector DBs