# Staff Engineer, Machine Learning
**Company:** [Poshmark](https://scaleengineer.com/companies/poshmark)
Staff Engineer, Machine Learning at Poshmark is a senior role within the Big Data team focused on building world-class machine learning systems and algorithms that directly impact the user experience for 165 million community members on the leading social commerce fashion platform. You'll design and own scalable ML pipelines, work across data science and engineering teams to democratize data, and solve critical business problems using cutting-edge distributed systems and machine learning frameworks. This position requires 8+ years of software development experience with 5+ years of applied machine learning expertise, strong software engineering fundamentals, and proficiency with big data technologies like Spark, SparkML, and cloud-scale data platforms.
**Role:** ML Engineer
**Seniority:** Staff
**Locations:** IN Tamil Nadu (Chennai) - Office
**Salary:** 200000–280000 USD
[Apply](https://jobs.ashbyhq.com/poshmark/b13d536d-bb05-4173-a17d-6ed55a2991c7)
Canonical: https://scaleengineer.com/jobs/poshmark/staff-engineer-machine-learning-b13d536d
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## Responsibilities

- Algorithm Development and Research: Explore large datasets and conduct research to develop algorithms and machine learning models that solve complex business problems. Analyze data patterns, implement novel approaches to feature engineering, and design statistical models that drive measurable improvements in product recommendations, search relevance, and user engagement across Poshmark's 165 million member marketplace.
- Scalable ML Systems Architecture: Design and implement highly scalable machine learning applications capable of processing large volumes of data in real-time and batch processing environments. Build production-grade systems that handle the scale of Poshmark's commerce platform, ensuring low latency for real-time predictions and efficient batch processing for offline analysis.
- Cross-Functional Collaboration and Product Delivery: Partner with data science, product, and engineering teams to translate business requirements into machine learning solutions. Lead end-to-end delivery of ML-powered data products, manage technical discussions with stakeholders, and ensure alignment between engineering constraints and business objectives to maximize impact.
- Engineering Excellence and Best Practices: Establish and advocate for best practices in machine learning development, model deployment, monitoring, and maintenance. Create reusable tools, frameworks, and libraries that enable robust, maintainable feature delivery across teams. Conduct code reviews, design technical solutions, and mentor junior engineers on ML systems engineering principles.
- Distributed Systems Architecture and Optimization: Design and improve system architecture to ensure horizontal scalability at all layers, including data ingestion, feature processing, model training, and inference serving. Optimize performance bottlenecks, implement distributed computing patterns, and manage infrastructure-level challenges to support business growth at scale.

## Requirements

### education

- {"name":"Bachelor's Degree in Computer Science or Related Field","description":"Formal education in Computer Science, Mathematics, Statistics, Physics, or related engineering disciplines providing strong foundational knowledge in algorithms, data structures, and computational theory."}
- {"name":"Advanced Coursework or Certifications (Preferred)","description":"Advanced studies in machine learning, statistics, linear algebra, or distributed systems through graduate programs, specialized certifications, or professional coursework demonstrates commitment to technical depth in the field."}

### technical

- {"name":"Machine Learning Algorithms and Modeling","description":"Deep expertise in supervised and unsupervised learning algorithms, statistical modeling, feature engineering, model evaluation methodologies, and algorithm selection for specific business use cases. Proven ability to implement and optimize models including regression, classification, clustering, recommendation systems, and neural network architectures."}
- {"name":"Distributed Computing and Big Data Technologies","description":"Advanced proficiency with Apache Spark, SparkML, and distributed data processing frameworks. Experience designing and implementing data pipelines that process terabyte-scale datasets, optimizing shuffle operations, and managing resource allocation in distributed computing environments."}
- {"name":"Software Engineering Fundamentals","description":"Solid foundation in software design patterns, object-oriented programming, system design, testing strategies, and software architecture. Ability to write clean, maintainable, and well-tested production code that meets enterprise standards for reliability and performance."}
- {"name":"Data Pipeline and Workflow Orchestration","description":"Experience building and maintaining production data pipelines using orchestration tools like Apache Airflow. Knowledge of scheduling, monitoring, error handling, and dependency management in complex data workflows."}
- {"name":"Large-Scale Data Platforms","description":"Working knowledge of data warehousing solutions, columnar databases, and real-time processing systems. Experience with platforms like Amazon Redshift, MongoDB, and stream processing technologies such as AWS Kinesis for handling continuous data flows."}

### experience

- {"name":"8+ Years of Software Development Experience","description":"Comprehensive background in professional software engineering, including experience building production systems, managing technical complexity, working with version control, CI/CD pipelines, and collaborating on large-scale projects with multiple engineers."}
- {"name":"5+ Years of Applied Machine Learning Industry Experience","description":"Proven track record applying machine learning to real-world business problems at scale. Experience taking models from research to production, managing model performance in live systems, addressing data quality challenges, and delivering measurable business value through ML solutions."}
- {"name":"Distributed Systems and Big Data Engineering","description":"Hands-on experience with distributed system architecture, managing data at scale, working with cluster computing environments, and understanding challenges of horizontal scalability, fault tolerance, and consistency in large-scale systems."}

## Skills

### required

- {"name":"Python Programming","description":"Expert-level Python development for machine learning applications, data analysis, and scientific computing. Proficiency with Python libraries including NumPy, Pandas, Scikit-learn, and ecosystem tools for rapid prototyping and production deployment."}
- {"name":"Scala Development","description":"Strong Scala programming skills for distributed computing with Apache Spark, including functional programming paradigms, working with Scala collections, and writing efficient Spark applications for large-scale data processing."}
- {"name":"Machine Learning Frameworks","description":"Hands-on expertise with production ML frameworks including TensorFlow, PyTorch, and Keras. Ability to build, train, evaluate, and deploy neural networks and deep learning models for various problem domains."}
- {"name":"Apache Spark and SparkML","description":"Advanced proficiency in Apache Spark for distributed data processing and SparkML for scalable machine learning. Experience optimizing Spark jobs, tuning performance, and implementing machine learning pipelines at enterprise scale."}
- {"name":"SQL and Database Querying","description":"Advanced SQL skills for querying, analyzing, and transforming data across relational and non-relational databases. Ability to optimize queries, understand query execution plans, and work efficiently with structured data at scale."}

### preferred

- {"name":"Real-Time Stream Processing","description":"Experience with streaming data architectures and technologies such as AWS Kinesis, Apache Kafka, or Apache Flink for processing continuous data streams and implementing low-latency ML inference systems."}
- {"name":"Recommendation Systems","description":"Background building collaborative filtering, content-based, or hybrid recommendation systems that power personalization features in consumer applications or e-commerce platforms."}
- {"name":"MLOps and Model Serving","description":"Experience with machine learning operations including model versioning, experiment tracking, A/B testing frameworks, model monitoring in production, and serving infrastructure for real-time predictions at scale."}
- {"name":"Search and Information Retrieval","description":"Understanding of search engine architecture, ranking algorithms, relevance optimization, and information retrieval techniques valuable for e-commerce and marketplace platforms."}
- {"name":"Natural Language Processing","description":"Applied experience with NLP techniques for text classification, entity recognition, semantic search, or language understanding to enhance product discovery and user engagement."}
- {"name":"Cloud Infrastructure","description":"Familiarity with cloud platforms like AWS, including services for compute (EC2), storage (S3), data warehousing (Redshift), and managed machine learning services for building scalable systems."}

## Tech stack

### tools

- {"name":"Apache Airflow","description":"Workflow orchestration platform for scheduling, monitoring, and managing complex data and machine learning pipelines with dependency management"}
- {"name":"Jenkins","description":"Continuous integration and deployment tool for automating testing, building, and deploying machine learning models and data pipelines"}
- {"name":"AWS Kinesis","description":"Real-time data streaming service for ingesting and processing continuous data streams, enabling low-latency machine learning applications"}

### others

- {"name":"Git Version Control","description":"Distributed version control system for managing code repositories, collaboration, and tracking changes in machine learning and engineering projects"}
- {"name":"Jupyter Notebooks","description":"Interactive computing environment for exploratory data analysis, algorithm prototyping, visualization, and machine learning experimentation"}
- {"name":"Docker and Containerization","description":"Container technology for packaging machine learning applications with dependencies, ensuring reproducibility and simplified deployment across environments"}

### databases

- {"name":"MongoDB","description":"NoSQL document database used for storing flexible, semi-structured data supporting rapid iteration in data-driven applications"}
- {"name":"Amazon Redshift","description":"Cloud-based data warehouse for analytical workloads, storing large volumes of structured data for machine learning feature engineering and analysis"}
- {"name":"Druid","description":"Real-time OLAP datastore optimized for fast aggregations and ad-hoc queries on large datasets, enabling real-time analytics and dashboarding"}

### languages

- {"name":"Python","description":"Primary language for machine learning development, data analysis, algorithm implementation, and rapid prototyping of ML solutions"}
- {"name":"Scala","description":"Used with Apache Spark for distributed computing, data processing, and building scalable machine learning applications in the Spark ecosystem"}
- {"name":"SQL","description":"Essential for querying data warehouses, writing analytics queries, and data preparation for machine learning pipelines"}

### frameworks

- {"name":"Apache Spark","description":"Distributed computing framework for large-scale data processing and implementing scalable ETL and machine learning pipelines"}
- {"name":"SparkML","description":"Machine learning library built on Apache Spark for building distributed machine learning pipelines that scale to large datasets"}
- {"name":"TensorFlow","description":"Deep learning framework for building and training neural networks, used for advanced machine learning models and computer vision applications"}
- {"name":"PyTorch","description":"Modern deep learning framework known for flexibility and ease of use in research and production machine learning applications"}
- {"name":"Keras","description":"High-level neural network API for rapid prototyping of deep learning models, often used with TensorFlow backend"}

## Benefits

### benefits

- {"name":"Comprehensive Health and Wellness Coverage","description":"Medical, dental, and vision insurance for employees and eligible dependents, plus mental health and wellness programs supporting holistic employee wellbeing"}
- {"name":"401(k) Retirement Plan","description":"Company-sponsored retirement plan with employer matching contributions, helping you build long-term financial security and retirement savings"}
- {"name":"Equity Compensation and Stock Options","description":"Participation in company equity programs that align your success with Poshmark's growth, creating wealth-building opportunities as a public company"}
- {"name":"Paid Time Off and Flexible Scheduling","description":"Generous vacation, personal days, and flexible work arrangements supporting work-life balance and personal priorities"}
- {"name":"Professional Development and Learning","description":"Budget for conferences, courses, certifications, and training programs supporting continuous learning and career growth in machine learning and distributed systems"}
- {"name":"Collaborative Culture and Innovation","description":"Opportunity to work on high-impact problems at scale within a supportive engineering culture that values collaboration, technical excellence, and creative problem-solving"}

## Compensation

- **max:** 280000
- **min:** 200000
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

- {"name":"Initial Screening and Recruiter Conversation","description":"Phone or video screening with Poshmark recruiter to discuss background, experience with machine learning systems at scale, familiarity with distributed computing, and overall fit with the role and team dynamics"}
- {"name":"Technical Deep Dive Assessment","description":"Comprehensive evaluation of machine learning expertise covering algorithm design, system architecture thinking, and distributed systems knowledge. Discussion of past projects, technical challenges overcome, and approach to scalable system design"}
- {"name":"Machine Learning Systems Design Interview","description":"Case study-style interview where you design end-to-end machine learning systems. Topics may include building recommendation engines for e-commerce, designing real-time prediction systems, or architecting scalable feature pipelines"}
- {"name":"Code Implementation or Technical Problem Solving","description":"Demonstration of coding ability in Python or Scala, working through machine learning or distributed systems problems. This may involve implementing algorithms, optimizing data pipelines, or solving real-world technical challenges"}
- {"name":"Team and Leadership Collaboration Interviews","description":"Conversations with current ML/Data Engineering team members and cross-functional partners to assess collaboration style, mentorship approach, and ability to influence and drive technical decisions across teams"}
- {"name":"Executive Leadership Conversation","description":"Discussion with engineering leadership regarding strategic vision, long-term impact on machine learning initiatives, leadership philosophy, and how you see ML infrastructure evolving to support business growth"}

## Full description
## 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.

# 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.