Staff Machine Learning Engineer

Job Description

CNN invented cable news in 1980, defined online news in 1995 and is now taking the next step in expanding what news can be by launching CNN+. With an unrivaled global reach, storytelling, and world class talent, we are building CNN+, a streaming product that will grow the reach and scope of the CNN brand in a way that no one else is doing.

We are journalists, designers and technologists, all united by a powerful mission to inform, engage and empower the world. Join the most trusted and recognized name in news as we build our next chapter.

Staff Machine Learning Engineer

We are looking for a Staff Machine Learning Engineer to help us significantly scale how we experiment, build, and collaborate on ML-based data products. We are currently optimizing our first ML platform to help accelerate our experimentation and model production, and we would love for you to join us in making a huge impact on the overall organization. This is a remote position, and candidates who live near a CNN office are welcome but not required to work from an office as desired.

Here's some of the problems you'll be helping us solve:
• The news cycle moves fast. What can we learn from readers' engagement with breaking news to recommend writing or video which deepens their understanding?
• How can we create a virtuous recommender system which drives diversity in people's reading habits?
• What's the best way for a machine learning group to work closely with journalists and editors to keep our audience engaged and informed?

What You'll Do
• Prototype and train new models to serve recommendations-oriented products for learning and experimentation
• Collaborate with other ML and software engineers to develop and improve core components, infrastructure and architecture of our ML Platform to train, deploy, and serve models at scale
• Author, test, review, and optimize production-level code in Python and Golang while executing best practices in version control and code integration
• Use and build upon open-source cloud computing technologies
• Guide data scientists, engineers, product teams and other key stakeholders and drive ML projects from conception to completion
• Mentor ML Engineers across the organization
• Initiate and lead architecture improvements for our ML products and infrastructure in collaboration with other ML and software engineers

Who You Are
• You can design and build real time distributed systems for machine learning at scale.
• You have experience guiding and collaborating with data scientists, data engineers, backend engineers, and product stakeholders on machine learning products.
• You are excited about complex architecture/infrastructure for training, deploying, evaluating, and serving models, and finding ways to make all of it faster by using the latest technologies.
• You enjoy keeping up with the latest ML research but get more satisfaction from building things with real user impact.
• You understand the constraints of working with a growing team and thrive in an environment that is fast-paced and sometimes scrappy.
• You understand that serving a user-facing model comes with a set of restrictions and you know how to be creative to solve them.
• You can work independently when needed.
• You can contribute to multiple team projects simultaneously.
• You have a deep curiosity and are proactive in seeking innovative solutions to business problems.
• You are highly communicative with your team and collaborators.
• You have collaborated with others on a team to design and build web scale distributed systems.

Things You Should Know
• How to write robust code in Python, Golang or an equivalent modern programming language
• How to wrangle data - working with databases (relational or columnar), how to write SQL, how to work with a data pipeline, and other big data technologies and working with large data sets
• How to take a product problem and choose the right ML approach to prototype, evaluate, and tweak a solution quickly before taking it to production scale
• Commonly used machine learning frameworks (like Keras, PyTorch or TensorFlow) and libraries (like scikit-learn)

Things You Might Know
• Experience with AWS and MLOps tools such as Sagemaker, MLFlow and Metaflow
• Experience with graph databases
• Experience with containerization (Docker) and container-orchestration systems such as Kubernetes
• Experience with Terraform, Azure or a similar IaC (infrastructure-as-code) tool
• If you don't know any of these, that's OK- you'll get the opportunity to learn them on the job once you join!

Jobcode: Reference SBJ-d93vnm-18-226-187-199-42 in your application.