Job Description

In the Search & Recommendation team, we work together to productionalize custom machine-learning models that can drive product vision and customer impact at scale. We are looking for MLE who are product driven, and are passionate about making ML innovations in areas such as; Ranking, Optimization, Natural Language Processing, Information Retrieval, Graph Learning, Reinforcement Learning to help improve the StockX buyer/seller experience!

About the Role

Example Projects:

• Develop embeddings to collect salient signals of our customers, product, and user interactions.
• Extract real-time signals and multi-modality data (i.e, content and image) from our 5M+ product catalog images and 1M+ listings. Understand semantic content, aesthetic style, materials for retrieval, ranking and optimization.
• Build a real-time, in-session personalization recommendation system.
• Implement and compare supervised learning models (i.e, LR, GBDT, and DNNs) or ensembles of models, to improve metrics, often with multiple contending objectives (i.e, relevance, degree of personalization, average value of orders, repeated frequencies/purchases).
• Develop models with custom architecture or objective functions that target StockX-specific problems, such as recommendation system, personalized search, revenue optimization, seller fairness, seasonality, etc.
• Develop brand-new learning frameworks for query suggestions to understand buyer experience.

What You'll Do:

• Apply the latest advances in deep learning and machine learning to improve buyer and seller experiences on StockX.
• Prototype, optimize, and productionize large-scale ML models that help deliver key results in search experience.
• Conduct A/B experiments to validate ML models and pipelines.
• Work closely with product managers, Data scientists/engineers, full-stack engineers, and designers on product teams to deliver content to tens of millions of users.

Qualifications

Basic requirements:

• Experience with object-oriented or functional software development
• Experience working with AWS or other cloud providers
• Experience with big data platforms like Spark or Databricks
• Experience with machine learning libraries such as TensorFlow, PyTorch, or MXNet
• You have dealt with data exploration, analysis, and feature engineering
• You have relentlessly high standards for the products you deliver
• Work effectively in an agile development process

Preferred requirements:

• You have a postgraduate degree in Computer Science or related engineering fields plus 3+ machine learning experience, or 5+ years of practical machine learning experience.
• Experience with Kubernetes and Docker for productionalizing models
• You have experience in building machine learning systems at scale.
• You have experience in using AWS Cloud Platform, Databricks and/or OpenSearch.
• You have experience in building production search, recommendations, advertising, or general e-commerce systems.