AI Engineer

Redfin Redfin · Seattle · Lake Vista, TX

This role focuses on building the ML engineering platform and services for a real estate company. Responsibilities include developing algorithms, training simulations, hosting ML models, data pipelines, and monitoring ML applications. It involves both data engineering and model serving aspects.

What you'd actually do

  1. Collaborate with data scientists to develop algorithms and tools for training and running simulations
  2. Build services to interact with machine learning models through simulations
  3. Participate in code reviews to ensure code quality and share best practices
  4. Develop services that host the trained models and work with other application teams to integrate them into business processes
  5. Gather and analyze large datasets and develop data model pipelines

Skills

Required

  • Master’s degree in software development, computer science, algorithm design, artificial intelligence, or machine learning or equivalent experience

Nice to have

  • 1 year of experience in machine learning and using libraries such as Scikit-learn, TensorFlow, Caffe, Keras, etc.
  • 1 year of experience working with large datasets, structured and unstructured
  • 1 year of programming experience, including Java, Scala, C/C++, or Python
  • 1 year of experience with the Hadoop ecosystem (Apache Hive, Pig, HBase and Kafka)
  • 1 year of experience with distributed computing platforms, such as Spark, and user interface frameworks, such as Angular or React
  • 1 year of experience with cloud computing providers such as AWS or Azure
  • Ph.D. in software development, computer science, algorithm design, artificial intelligence, or machine learning or equivalent experience
  • Proficiency in the Microsoft Office suite
  • Strong object-oriented programming skills, including proficiency in Java, Scala, C/C++ or Python
  • Knowledge of big data

Other signals

  • design and develop the platform and frameworks that facilitate automated data-driven decision-making
  • gather data, and determine statistical algorithms and models that a system can use to learn from experience, predict outcomes and make decisions
  • develop algorithms and tools for training and running simulations
  • build services to interact with machine learning models through simulations
  • develop services that host the trained models and work with other application teams to integrate them into business processes
  • gather and analyze large datasets and develop data model pipelines
  • develop algorithms that drive automated data-driven decision-making
  • build the tools for monitoring the performance of machine learning applications