Job description 

What You’ll Do

Work with model and platform teams to build systems that ingest large amounts of model and feature metadata that will feed into automated governance decisioning

Partner with product and design teams to build elegant and scalable solutions to speed up governance processes

Collaborate as part of a cross-functional Agile team to create and enhance software that enables state of the art, next generation big data and machine learning applications.

Leverage cloud-based architectures and technologies to deliver optimized ML models at scale

Construct optimized data pipelines to feed machine learning models

Use programming languages like Python, Scala, or Java

Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployments of machine learning models and application code

Advocate for software and machine learning engineering best practices

Function as a technical lead

As a people leader, you will be responsible for mentoring, coaching, providing feedback, building career plans and assessing performance for your direct reports enabling a high performance engineering team

Basic Qualifications

Bachelor’s Degree

At least 6 years of experience designing and building data intensive solutions using distributed computing

At least 4 years of experience programming with Python, Go, or Java

At least 2 years of experience building, scaling, and optimizing ML systems

At least 2 years of experience with the full ML Development Lifecycle using industry-recognized best practices

Preferred Qualifications

Master’s Degree or PhD in Computer Science, Electrical Engineering, Mathematics, or a similar field

3+ years of experience building production-ready data pipelines that feed ML models

3+ years of on-the-job experience with an industry recognized ML framework  such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow

2+ years of experience developing performant, resilient, and maintainable code

2+ years of experience with data gathering and preparation for ML models

practices, patterns, and automation

Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform

Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance

Contributed to open source ML software

Authored/co-authored a paper on a ML technique, model, or proof of concept