Machine Learning Infrastructure Engineer:

We’d love you to bring:

• Background in software engineering, specifically in the context of a data science and machine learning team
• Experience with: Python, DS/ML libraries such as TensorFlow
• Experience automating & monitoring complex workflows on large datasets, with an emphasis on simplicity whenever possible
• Knowledge of cloud products and how they fit into ML (we’re in AWS)
• Excellent problem solving skills, and a desire to dig into hard problems around running machine learning systems at scale
• We use Apache Spark, Docker, Terraform, but you don’t need prior experience with these
• Have proven success working 100% remote in prior positions & are experienced working with a distributed, national team
• We encourage you to apply even if you don’t have every listed requirement 

As a Machine Learning Infrastructure Engineer you will: 

• Build our machine learning platform that powers the Knock Home Swap™ product. This product ingests large quantities of data to accurately predict home values across the nation. You’ll partner with Machine Learning Engineers, offering tools, infrastructure, data, and services to enable our ML scalability. 
• Develop, extend, and maintain systems used in our machine learning infrastructure, such as proprietary geospatial systems and training workflow orchestration. You'll work on automation of training, validation, and deployment of ML models.
• Utilize infrastructure-as-code technology such as Terraform to automate provisioning of resources.
• Optimize and debug ML training support systems; as well as, develop libraries and tools to aid ML engineers day-to-day work. Wrangle large datasets and make them available for training and inference via both bulk files and APIs. Implement monitoring and alerting for ML systems.
• Continually learn, grow, and expand your knowledge, while also supporting others learning experiences; act as a mentor and be a resource for others inside and outside of your team. You will take on ad-hoc projects as they arise & always be willing to support your team where they need you.