Staff Engineer, Machine Learning

Staff Engineer Machine Learning

Big Data team is a central player in the Poshmark organization. Our mission is to

build a world-class big data platform to bring value out of data for us and for our customers. Our goal is to democratize data, support exploding business, build data and ML pipelines to fuel existing and new business critical initiatives.

We are looking for exceptional, creative and passionate Machine Learning engineers to join our ML and Big Data Team. You will be responsible for building and owning the next-generation of algorithms and systems that would have critical business impact for Poshmark and improve the user experience for our millions of users

Responsibilities

Explore large datasets, research and develop algorithms / models to solve interesting business problems.

Design and code highly scalable, machine learning applications processing large volumes of data.

Collaborate with multiple teams - data science, business, engineering and help deliver Machine learning based Data products across the company.

Develop best practices and tools to enable robust delivery of features

Design and improve architecture in order to ensure horizontal scalability at all layers

Ideal Candidate

Strong background in Machine Learning with deep understanding of algorithms and modeling techniques

8+ years of overall software development experience with at least 5+ years of industry experience applying Machine Learning to concrete problems

Great coding skills and strong software development experience with Big Data technologies & Machine Learning frameworks like SparkML, TensorFlow, PyTorch, Keras

Understanding of distributed systems, and large scale engineering challenges is plus.

Technologies we use

Scala, Python

MongoDB, Redshift, Druid

Airflow, Jenkins

Spark, SparkML, Kinesis

Why Poshmark?

Poshmark is a leading social commerce platform for the next generation of retailers and shoppers. Through technology, our mission is to build the world’s most connected shopping experience, while empowering people to build thriving retail businesses.