Senior Software Engineer II, Machine Learning
ML Engineer · Senior · Full Time
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Role
What you'll do.
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.
Responsibilities
- 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.
Qualifications
What we look for.
Technical
Computer Science Fundamentals
Strong 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 Fundamentals
Deep 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 Programming
Expert-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 Design
Strong 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 Field
Formal 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 Experience
Demonstrated 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 Expertise
Comprehensive 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 Implementation
Hands-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 Systems
Strong 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
Python
Production-level Python expertise for ML model development, data processing, and algorithm implementation with proficiency in pandas, NumPy, and scikit-learn ecosystems.
SQL
Advanced SQL for data exploration, feature engineering, and building reproducible data pipelines. Ability to optimize queries for performance at scale on large datasets.
Machine Learning Frameworks
Hands-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 Orchestration
Experience 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 Monitoring
Practical 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 Infrastructure
Experience working with cloud infrastructure, containerization, and orchestration platforms for deploying and scaling ML services. Understanding of cloud-native architectures and resource optimization.
Preferred
Large Language Models (LLMs)
Nice to haveProduction 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 Spark
Nice to haveHands-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 Scala
Nice to haveProficiency 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 MLOps
Nice to haveExperience 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 Embeddings
Nice to haveExperience 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 Streaming
Nice to haveFamiliarity with message queue systems like RabbitMQ and streaming platforms for building real-time ML inference pipelines and asynchronous processing architectures.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 160,000 – 220,000
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.
Full posting
Original listing.
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.
Senior Machine Learning Engineer
Poshmark is a leading social marketplace for new and secondhand styles for women, men, kids, home, and more. By combining the human connection of physical shopping with the scale, ease, and selection benefits of ecommerce, Poshmark makes buying and selling simple, social, and fun.
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 digitisation 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
Why Poshmark?
Poshmark is a leading social marketplace for new and secondhand style for women, men, kids, pets, home, and more. By combining the human connection of physical shopping with the scale, ease, and selection benefits of ecommerce, Poshmark makes buying and selling simple, social, and sustainable. Its community of more than 70 million registered users across the U.S., Canada, and Australia is driving the future of commerce while promoting more sustainable consumption. For more information, please visit www.poshmark.com, and for company news and announcements, please visit investors.poshmark.com. You can also find Poshmark on Instagram, Facebook, Twitter, Pinterest, and YouTube.
About Us
At Poshmark, we’re constantly challenging the status quo and are looking for innovative and passionate people to help shape the future of Poshmark. We’re disrupting the industry by combining social connections with e-commerce through data-driven solutions and the latest technology to optimize our platform. We’re nothing without our amazing team who deliver an unparalleled social shopping experience to the millions of people we connect each day.
We built Poshmark around four core values:
Focus on People to create empowered communities that drive success;
Together we Grow to support each other to strive for our dreams;
Lead with Love to foster genuine connections built upon a foundation of respect; and
Embrace your Weirdness to accept and empower one another on their own unique journey.
We’re invested in our team and community, working together to build an entirely new way to shop. That way, when we win, we all win together. Come help us build the most connected shopping experience ever.
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