Poshmark

Sr. Engineer, Machine Learning

Poshmark2 weeks ago
Location

Redwood City, California, USA

Type

Full Time

Salary

USD 151,194 – 214,587

Level

Senior

Role

Machine Learning Engineer

Posted

Mar 1, 2026

Full TimeSenior

The role

Summary

Poshmark is seeking a Senior Machine Learning Engineer to join their innovative ML team, responsible for building a world-class machine learning platform that drives value across multiple business domains including search, personalization, fraud detection, and catalog digitization.


What we look for

Technical

ML Model ExpertiseProven experience with regression, classification, tree-based, neural network, and sequence-based models
Production MLAbility to deploy and monitor machine learning models at scale

Education

Computer ScienceBachelor's or Master's degree in Computer Science, Statistics, or related field

Experience

ML Experience4+ years of applying Machine Learning to large-scale problems
System ArchitectureUnderstanding of big data technologies and system design

Skills

Required skills

Machine LearningComprehensive understanding of ML algorithms and model development
PythonStrong programming skills for ML model implementation
SQLData querying and manipulation
ML Lifecycle ManagementEnd-to-end machine learning project management

Nice to have

Large Language ModelsExperience with LLMs and prompt engineering
Big Data TechnologiesFamiliarity with Spark, EMR, S3, Airflow
Vector DatabasesKnowledge of embedding techniques and vector storage

Compensation & benefits

Salary

USD 151,194 – 214,587 (annual)

Stock options

Available

Benefits

Competitive Compensation

Comprehensive salary package for senior machine learning engineers

Professional Development

Opportunities to work on cutting-edge ML technologies and innovative projects

Tech Community

Collaboration with a dynamic and innovative machine learning team


Interview process

  1. 1
    Initial Screening Resume and background review
  2. 2
    Technical Phone Screen Machine learning and coding fundamentals assessment
  3. 3
    Technical Interview Deep dive into ML expertise, system design, and problem-solving skills
  4. 4
    On-site/Virtual Interviews Multiple rounds with ML team, engineering leadership, and cross-functional stakeholders
  5. 5
    Final Interview Meeting with senior leadership and final decision-making

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