About the position

This position is a hybrid role requiring employees to work from our headquarters location in Seattle, WA every Tuesday and Wednesday, and remote all other days. Hybrid from Seattle is the preferred location but this role is open to fully remote candidates. Redfin is revolutionizing the \$75 billion real estate industry. We use data, beautiful software, and innovative design to put customers first at every step in the home-buying and selling process. Get ready to dive headfirst into our award-winning website and mobile apps, solving complex business problems in a highly visible, customer-centric way. If you value doing great work in a collaborative environment, join our team! The Applied Machine Learning group at Redfin works towards redefining real estate in the customer’s favor using machine learning. We work on foundational problems in the real estate space including recommendations (“Where should I live?”) and price estimation (“How much is a home worth?”). The Brokerage Recommendations product alone, which we power drives 27% of all traffic to Redfin platforms. We have real estate data at a national level and work across various domains in machine learning using large-scale multi-modal property data (documents, images, text, video, 3D scans etc.). Our team also owns and maintains end-to-end production-grade large-scale machine learning infrastructure and systems serving hundreds of millions of consumers. As a Senior Machine Learning Engineer for the Applied Machine Learning Team, you’ll breathe life into our research by transforming prototypes into high-performance production systems, building the automated MLOps pipelines and real-time optimizations that keep our models running at scale. As the strategic bridge between research and engineering, you’ll own the health of our valuation and recommender systems to ensure they remain fast, reliable, and impactful for millions of users.

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

• You have 5+ years of software engineering experience, with at least 2 years specifically focused on deploying and scaling machine learning models in production environments.
• You are highly proficient in Python and capable of writing production-grade, modular code.
• You possess a deep understanding of the end-to-end ML lifecycle, including training versus inference workflows, feature stores, and model versioning.
• You are competent with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. While you may not be building architectures from scratch, you can effectively debug, tune, and optimize models for inference.
• You are competent with monitoring, observability, and production maintenance. You can effectively set up and manage logging, metrics, and alerting pipelines, debug production issues, and ensure system reliability and performance in high-scale feature store
• You have hands-on experience with Docker and Kubernetes. You understand how to deploy, manage, and scale containers within a cluster environment.
• You have experience implementing model monitoring and operational rigor, including tracking data drift and latency, as well as utilizing A/B testing frameworks to validate model performance in the wild.
• You are proficient in SQL and familiar with distributed data processing tools like Spark or Kafka to ensure that data reaching the model is high-quality and consistent with training distributions.

Benefits

• Competitive compensation packages with a salary, bonuses, and restricted stock grants.
• Generous benefits, including paid vacation, medical, dental, and vision insurance, and paid family leave.
• A high-growth company, providing opportunities for continued professional development and growth.