Staff Machine Learning Engineer

Looking for a Staff Machine Learning Engineer to help build StockX’s next generation ML Platform. 

The Machine Learning (ML) team is responsible for building a thoroughly thought out architecture for data ingestion, feature engineering, model training, and productionalization. Post-production, we ensure continued success via MLOps techniques like data validation, drift detection, and model management. We currently have a wide array of ML problems including personalization, computer vision, natural language processing, anomaly/fraud detection, and forecasting.

As one of the newest teams at StockX we have a large amount of autonomy and freedom to build top notch machine learning systems that will allow us to test hypotheses and iterate on models with agility. Come be a part of the next stage for StockX as we take our next steps into the world of machine learning and artificial intelligence.

Responsibilities

• Improve overall data quality for use by multiple teams
• Work cross functionally to deliver end-to-end ML products
• Balance pragmatic engineering decisions with advancing ML model capabilities
• Mentor team members to help level up the overall ML and engineering expertise
• Design data collection for solving diverse ML problem areas
• Foster a data-centric engineering culture
• Deploy endpoints for use by services and front end teams
• Perform A/B testing of models in production
• Utilize MLOps methodologies for maintenance

Requirements

• Experience working with AWS or other cloud providers
• Experience with big data platforms like Spark
• Experience with machine learning libraries such as TensorFlow, PyTorch, or MXNet
• You have solved data quality issues across multiple teams
• You can architect data platforms for use 
• You have relentlessly high standards for the products you deliver

Nice to Have

• Experience with transfer learning for computer vision applications
• Experience with applying deep learning to recommender systems
• Experience maintaining ML pipelines with tools like MLflow and Kubeflow
• Experience working with message queue systems such as Kafka or Kinesis
• Experience working with stream processing technologies such as Spark or Flink