Staff Machine Learning Engineer

Staff Machine Learning Engineer

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
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