Remote Senior Applied Machine Learning Engineer - Applied Machine Learning Team

Redfin is revolutionizing the real estate industry by utilizing data and innovative design to enhance the home-buying process. The Senior Applied Machine Learning Engineer will transform research prototypes into high-performance production systems and implement MLOps best practices to optimize machine learning models.

Responsibilities:

• You will productionize models by converting research-grade code into performant, clean, and maintainable production systems
• You will implement MLOps best practices, including CI/CD for machine learning, automated retraining pipelines, and robust model versioning
• You will optimize models for inference to ensure high-speed performance and efficiency in real-time environments
• You will monitor models in production, proactively identifying and mitigating issues related to data drift, concept drift, and system latency
• You will co-create the next generation of data-driven insights for automated valuation models (AVM) and recommender systems
• You will identify and implement iterative improvements to the machine learning models that power production-scale, customer-facing experiences
• You will serve as a technical bridge, assisting other engineers and stakeholders in understanding and applying data science methodologies and findings across the organization
• You will build data products and analytical tools that drive critical business metrics and revenue growth, directly impacting the home buying and selling experience

Requirements:

• 5+ years of software engineering experience, with at least 2 years specifically focused on deploying and scaling machine learning models in production environments
• Highly proficient in Python and capable of writing production-grade, modular code
• Deep understanding of the end-to-end ML lifecycle, including training versus inference workflows, feature stores, and model versioning
• Competent with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn
• Competent with monitoring, observability, and production maintenance
• Hands-on experience with Docker and Kubernetes
• Experience implementing model monitoring and operational rigor, including tracking data drift and latency
• Proficient in SQL and familiar with distributed data processing tools like Spark or Kafka