Sr. Engineer, Machine Learning

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.

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).

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

Desired Skills & Experience:

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

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

Our Techstack:

Pytorch, Tensorflow, Sklearn

Flask, Fastapi, Gunicorn, Quarkus

MLFlow, Sagemaker, Kibana, Airflow, Databricks, Grafana, Datadog

Docker, Kubernetes, Jenkins

Redis, Redshift, MongoDB, Spark, Kafka, RabbitMQ, Milvus