Poshmark

Software Engineer II, Machine Learning

Poshmark2 weeks ago
Location

IN Tamil Nadu (Chennai) - Office

Type

Full Time

Salary

USD 145,000 – 185,000

Level

Mid

Role

ML Engineer

Posted

Jul 6, 2026

Full TimeMid

The role

Summary

Software Engineer II, Machine Learning is a mid-level role at Poshmark focused on productionizing machine learning models and building infrastructure to democratize ML across the organization. This position requires 2-3 years of software engineering experience with data-intensive applications, strong understanding of ML concepts, and expertise in tools like Spark, SQL, and backend API development. The role is central to enabling Poshmark's mission to leverage machine learning across search, personalization, fraud detection, and catalog digitization for the 165-million-member fashion marketplace.

What you'll do

Build ML Infrastructure and Tools: Design, develop, and maintain tools and infrastructure to democratize machine learning across Poshmark's organization. Create scalable systems that enable data scientists and engineers to efficiently deploy, monitor, and manage ML models with varying requirements and complexities.
Productionize Machine Learning Models: Collaborate with Data Science and cross-functional Engineering teams to take machine learning models from research and development into production environments. Implement best practices for model versioning, testing, validation, and deployment pipelines to ensure reliability and performance.
Platform Maintenance and Evolution: Maintain, monitor, and support existing ML platforms and systems. Continuously evaluate and adopt newer technology stacks, architectures, and frameworks to improve performance, scalability, and developer experience across the ML engineering ecosystem.
Design Backend APIs for ML Services: Engineer robust, well-documented backend APIs that expose machine learning models and services to internal and external consumers. Ensure APIs meet performance requirements, handle scale, and maintain data integrity across the platform.
Optimize Data Pipelines: Build and optimize data pipelines using Spark, SQL, and streaming technologies to support machine learning workflows. Ensure efficient data processing, transformation, and feature engineering for diverse ML use cases including search, personalization, and fraud detection.

What we look for

Technical

Data-Intensive Application DevelopmentProven experience building software systems that process, analyze, and serve data at scale. Strong foundation in handling large datasets, managing data pipelines, and optimizing performance in data-heavy environments.
Machine Learning Concepts and LifecycleSolid understanding of ML fundamentals including supervised and unsupervised learning, model evaluation metrics, cross-validation, hyperparameter tuning, and the complete ML lifecycle from problem definition through monitoring. Ability to collaborate effectively with data scientists and understand model requirements.
Apache Spark and SQL ProficiencyStrong hands-on experience with Apache Spark for distributed data processing and SQL for data querying and manipulation. Ability to write optimized queries, design efficient Spark jobs, and troubleshoot performance issues in data processing pipelines.
Backend API DevelopmentExperience designing and developing RESTful or GraphQL APIs, understanding of service-oriented architecture, and knowledge of API best practices including authentication, rate limiting, error handling, and documentation.
Python ProgrammingAdvanced proficiency in Python for machine learning, data processing, and backend development. Experience with writing clean, maintainable code following software engineering best practices.
Containerization and OrchestrationHands-on experience with Docker for containerizing applications and Kubernetes for orchestrating and managing containerized services in production environments.

Education

Computer Science or Related FieldBachelor's degree in Computer Science, Mathematics, Physics, or a related technical field. Equivalent professional experience with demonstrated expertise in software engineering and machine learning systems is highly valued.

Experience

Software Engineering with Data Systems2-3 years of professional software engineering experience specifically working with data-intensive applications, ML platforms, or analytics systems. Demonstrated ability to ship production code, handle real-world data challenges, and collaborate with cross-functional teams.
ML Model ProductionizationPrior experience taking machine learning models to production, understanding deployment challenges, and implementing solutions for model serving, monitoring, and versioning.
Collaboration in ML EcosystemsExperience working alongside data scientists and ML practitioners, understanding their workflow requirements, and building tools that enhance their productivity and model deployment capabilities.

Skills

Required skills

PythonExpert-level proficiency in Python for ML engineering, data processing, and backend development with understanding of performance optimization and software engineering best practices.
Apache SparkStrong hands-on experience with Spark for distributed computing, data processing at scale, and understanding of RDD, DataFrame, and SQL APIs.
SQLProficiency in writing complex SQL queries, optimizing database performance, and understanding data modeling concepts for analytics and ML workflows.
Backend API DevelopmentExperience building production-grade APIs using frameworks like Flask, FastAPI, or Django with understanding of REST principles, error handling, and API security.
Machine Learning FundamentalsStrong grasp of ML concepts including model training, evaluation, validation strategies, and understanding of common ML algorithms and their application scenarios.
Docker and KubernetesPractical experience containerizing applications with Docker and deploying to Kubernetes clusters for production workloads.
Data EngineeringUnderstanding of data pipelines, ETL processes, data modeling, and best practices for data quality and reliability in production systems.

Nice to have

MLflowExperience with MLflow for model tracking, experiment management, versioning, and deployment as part of the ML lifecycle. Understanding of model registry and production-ready ML practices.
AWS SageMakerFamiliarity with AWS SageMaker for model training, hosting, and pipeline orchestration. Experience with AWS services for ML workloads is valuable.
Message Queuing SystemsExperience with Redis, RabbitMQ, or Kafka for building asynchronous systems, event streaming, and managing distributed data pipelines.
Deep Learning FrameworksPrior experience with PyTorch or TensorFlow for building neural networks and understanding deep learning concepts and best practices for production deployment.
Stream ProcessingExperience with Kafka, Kinesis, or similar stream processing technologies for building real-time data pipelines and low-latency ML inference systems.
Workflow OrchestrationFamiliarity with Apache Airflow for scheduling, monitoring, and orchestrating complex data and ML workflows at scale.
NoSQL DatabasesExperience with MongoDB or similar NoSQL databases for handling unstructured data and building flexible data models for ML systems.
Cloud Data WarehousesExperience with AWS Redshift or similar cloud data warehouses for large-scale analytics and ML feature engineering.
Monitoring and ObservabilityFamiliarity with tools like Kibana, DataDog, or Prometheus for monitoring ML models, tracking performance metrics, and implementing alerting for production systems.
Technology AgilityDemonstrated ability to quickly learn and adopt new technologies, frameworks, and tools as business requirements evolve. Open mindset toward experimentation with emerging ML and data engineering solutions.

Compensation & benefits

Salary

USD 145,000 – 185,000 (annual)

Stock options

Available

Benefits

Professional Development and Learning

Access to cutting-edge machine learning tools, frameworks, and technologies. Opportunity to work on complex ML challenges at scale and collaborate with leading data scientists and engineers in the fashion tech industry.

Collaborative Innovation Environment

Work on mission-critical ML infrastructure that powers Poshmark's 165-million-member marketplace. Influence how machine learning drives value across search, personalization, fraud detection, and catalog digitization.

Growth and Impact

Position as a central player in the ML organization with direct influence on technical decisions and strategic direction. Opportunity to democratize ML across the company and mentor junior engineers.

Industry-Leading Problem Space

Tackle fascinating ML challenges in e-commerce, social commerce, and fashion tech. Work on problems that impact millions of users and shape the future of online shopping and sustainability through secondhand fashion.

Interview process

  1. 1
    Initial Screening Call — 30-minute conversation with a recruiter to discuss your background, interest in the ML Engineer II role, and career goals. This is an opportunity to learn about Poshmark's culture and the Machine Learning team's mission.
  2. 2
    Technical Phone Interview — 45-60 minute technical discussion with a member of the ML Engineering team covering system design, data processing concepts, ML deployment challenges, and your experience productionizing models. Expect questions about Spark, SQL, Python, and MLOps best practices.
  3. 3
    Take-Home Technical Assignment — Practical coding assignment focused on ML engineering challenges. Likely scenarios include data pipeline optimization, API design for ML model serving, or ML system architecture. Usually allocated 3-4 hours with flexibility on completion time.
  4. 4
    Onsite or Virtual Technical Rounds — Multiple technical interviews (typically 2-3 sessions) with different team members covering system design, ML concepts, software engineering practices, and data infrastructure. These sessions dig deeper into your technical depth and problem-solving approach.
  5. 5
    Team and Culture Fit Discussion — Conversation with team leadership or senior engineers to discuss collaboration style, learning approach, and alignment with Poshmark's values. Opportunity to ask detailed questions about team dynamics, technical direction, and growth opportunities.
Apply for this position

You'll be redirected to the company's application page

Poshmark

Poshmark

View all jobs

Poshmark is an American social commerce platform for buying and selling fashion. It enables users to create virtual closets and host live shopping events.

Redwood City, California, United StatesFounded 2011poshmark.com

Tech Stack

Languages
PythonSQL
Frameworks
FlaskPyTorchTensorFlowscikit-learnApache Spark
Databases
RedisRedshiftMongoDB
Tools
DockerKubernetesMLflowAWS SageMakerApache AirflowKibana
Other
KafkaRabbitMQAWS Kinesis

Interview Guides

14 guides available for Poshmark

Apply Now

On this page