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.
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 business, and use machine learning to drive value across the chain (Search, personalization, fraud detection, catalog digitization to name a few).
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
4+ 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 excellent understanding of 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
Big data systems – Spark, EMR, S3, AirFlow
Production experience with LLMs and prompt engineering
Experience with Flask, FastAPI, RabbitMQ, Embeddings and Vector DBs
Pytorch, Tensorflow, Sklearn
Flask, Fastapi, Gunicorn, Quarkus
MLFlow, Sagemaker, Kibana, Airflow, Databricks, Grafana, Datadog
Docker, Kubernetes, Jenkins
Redis, Redshift, MongoDB, Spark, Kafka, RabbitMQ, Milvus
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