Poshmark

Senior Software Engineer II, Machine Learning

Poshmark4 days ago
Location

IN Tamil Nadu (Chennai) - Office

Type

Full Time

Salary

USD 160,000 – 220,000

Level

Senior

Role

ML Engineer

Posted

Jun 25, 2026

Full TimeSenior

The role

Summary

Senior Machine Learning Engineer at Poshmark responsible for managing the complete ML lifecycle from data collection through deployment and monitoring on a social commerce platform serving 165 million community members. This role requires 4+ years of production machine learning experience with expertise in model development, system architecture, and cross-functional collaboration to drive machine learning solutions across search, personalization, fraud detection, and catalog digitization initiatives.

What you'll do

ML Lifecycle Management: Own the complete machine learning lifecycle including data collection, feature engineering, model training, validation, deployment, and post-production monitoring. Ensure models maintain performance metrics and reliability at scale while identifying opportunities for iteration and improvement.
Production Code Development: Write optimized, production-grade code for ML services that operate at enterprise scale. Implement robust error handling, comprehensive logging, and efficient resource utilization to ensure reliability and performance in high-traffic production environments serving millions of users.
Cross-Functional Collaboration: Partner closely with data scientists, QA engineers, infrastructure teams, and other engineering organizations to productionize machine learning models. Facilitate seamless handoffs and integration between research and production environments while maintaining clear communication channels.
Technology Evolution: Manage and support existing ML solutions while proactively evaluating and incorporating emerging technologies and methodologies. Stay current with industry developments in deep learning, large language models, and advanced ML architectures to keep Poshmark's platform competitive.
Technical Communication: Translate complex machine learning concepts into clear, actionable insights for both technical and non-technical stakeholders. Deliver compelling presentations and documentation that communicate model architecture decisions, performance trade-offs, and business impact to leadership and cross-functional teams.
System Architecture Design: Design and implement scalable ML system architectures that handle data pipelines, real-time inference requirements, and batch processing workloads. Leverage big data technologies and streaming architectures to support diverse use cases across search, personalization, fraud detection, and catalog digitization.

What we look for

Technical

Computer Science FundamentalsStrong grasp of core CS principles including algorithms, data structures, complexity analysis, and system design. Ability to design efficient solutions, optimize performance bottlenecks, and make informed trade-offs between competing technical requirements.
Machine Learning FundamentalsDeep understanding of supervised and unsupervised learning paradigms including regression, classification, tree-based methods, neural networks, and sequence models. Solid grasp of statistical concepts, overfitting prevention, cross-validation, and hyperparameter tuning techniques.
Python ProgrammingExpert-level Python proficiency for implementing ML algorithms, data processing, and model training. Experience with scientific computing libraries and ability to write clean, maintainable, and efficient code for production ML systems.
SQL and Database DesignStrong SQL skills for querying large datasets, building complex aggregations, and understanding database optimization. Knowledge of both relational database design and modern data warehouse architectures supporting analytics and ML feature extraction.

Education

Bachelor's Degree in Computer Science or Related FieldFormal education in Computer Science, Statistics, Mathematics, Physics, or equivalent discipline providing strong theoretical foundations for machine learning algorithm development and system design.
Master's Degree (Preferred)Advanced degree in Computer Science, Machine Learning, Data Science, Statistics, or related field that provides deeper theoretical knowledge and research experience. Not required but valued for candidates seeking to lead technical initiatives.

Experience

4+ Years Production ML ExperienceDemonstrated experience applying machine learning to concrete, large-scale problems in production environments. Track record of deploying ML models that deliver measurable business impact and handling real-world challenges including data quality issues, model drift, and scalability constraints.
Machine Learning Lifecycle ExpertiseComprehensive understanding of the complete ML workflow from problem formulation and data preparation through model development, validation, deployment, monitoring, and maintenance. Experience managing model performance degradation and implementing retraining pipelines.
Advanced ML Model ImplementationHands-on production experience with at least one advanced ML model type including Large Language Models (LLMs), Graph Neural Networks (GNNs), deep learning architectures, logistic regression, gradient boosting trees, or sequence-based models. Understanding of model selection trade-offs and optimization techniques.
Big Data and Distributed SystemsStrong knowledge of big data technologies including streaming architectures, distributed data processing, and data pipeline orchestration. Familiarity with platforms such as Apache Spark, cloud data warehousing solutions, and event streaming systems that support ML at scale.

Skills

Required skills

PythonProduction-level Python expertise for ML model development, data processing, and algorithm implementation with proficiency in pandas, NumPy, and scikit-learn ecosystems.
SQLAdvanced SQL for data exploration, feature engineering, and building reproducible data pipelines. Ability to optimize queries for performance at scale on large datasets.
Machine Learning FrameworksHands-on experience with PyTorch, TensorFlow, or scikit-learn for model development, training, and deployment. Understanding of framework-specific optimization and production serving capabilities.
Data Pipelines and OrchestrationExperience building and maintaining scalable data pipelines using orchestration tools. Understanding of ETL processes, data validation, and handling data at scale for ML feature stores and model training.
ML Model Deployment and MonitoringPractical experience deploying ML models to production environments and implementing monitoring systems to track model performance, data drift, and business metrics. Familiarity with model serving infrastructure and versioning strategies.
Cloud Platforms and InfrastructureExperience working with cloud infrastructure, containerization, and orchestration platforms for deploying and scaling ML services. Understanding of cloud-native architectures and resource optimization.

Nice to have

Large Language Models (LLMs)Production experience with LLMs, fine-tuning approaches, and prompt engineering techniques. Understanding of embedding-based representations and vector database systems for semantic search and retrieval augmented generation applications.
Apache SparkHands-on experience with Spark for distributed ML training, large-scale data processing, and feature engineering. Knowledge of Spark MLlib and optimization strategies for cluster computing environments.
Java or ScalaProficiency in Java or Scala for ML infrastructure development, data pipeline implementation, and building production ML systems that integrate with JVM-based ecosystems.
ML Operations and MLOpsExperience with ML operations practices including model versioning, experimentation tracking, continuous integration/deployment for ML, and feature management. Familiarity with MLOps platforms and CI/CD pipelines specific to ML systems.
Vector Databases and EmbeddingsExperience working with modern vector database systems like Milvus for similarity search and embedding management. Understanding of vector representation learning and applications in retrieval systems.
Message Queues and StreamingFamiliarity with message queue systems like RabbitMQ and streaming platforms for building real-time ML inference pipelines and asynchronous processing architectures.

Compensation & benefits

Salary

USD 160,000 – 220,000 (annual)

Benefits

Mission-Driven Impact

Join a platform transforming fashion commerce with 165 million community members where your ML work directly impacts shopping experiences, seller empowerment, and sustainable consumption patterns across the US, Canada, and Australia.

Cutting-Edge ML Challenges

Work on diverse machine learning applications including search ranking, personalization engines, fraud detection systems, and catalog digitization that serve real users at massive scale.

Collaborative ML Culture

Partner with world-class data scientists, infrastructure engineers, and cross-functional teams in a central ML organization responsible for democratizing data science and driving platform innovation.

Opportunity for Growth

Senior level position offering potential to mentor engineers, lead technical initiatives, and grow your career within a rapidly scaling machine learning organization at a high-growth company.

Inclusive Team Environment

Work in a culture built on four core values: Focus on People, Together we Grow, Lead with Love, and Embrace your Weirdness that empowers unique perspectives and fosters genuine connections.

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Poshmark

Poshmark

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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
PythonSQLJavaScala
Frameworks
PyTorchTensorFlowScikit-learnFlaskFastAPIGunicornQuarkus
Databases
RedisMongoDBAmazon RedshiftSparkKafkaRabbitMQMilvus
Tools
MLFlowAmazon SageMakerAirflowDatabricksDockerKubernetesJenkinsKibanaGrafanaDatadog
Other
Amazon S3Amazon EMRLarge Language Models (LLMs)Graph Neural Networks (GNNs)Recommendation Systems

Interview Guides

14 guides available for Poshmark

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