Senior Software Engineer, Machine Learning Platform
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Senior Software Engineer, Machine Learning Platform
We build machine learning powered recommendation, search, and classification tools used across CNN's platforms and experience. This year we have already launched two new products- "Watch Next", a video recommendation experience, and "Related Articles", an article recommendation feed.
We are looking for an experienced software engineer to help us significantly scale how we experiment, build, and collaborate on ML-based products. The team you would join operates and optimizes 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 daily habits of CNN users and on our overall organization.
Location: NY, SF, LA, Atlanta (preferred NY)
Manager: Ailish Byrne
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 journalism which deepens their understanding?
- What's the best way for a machine learning group to work closely with journalists and editors to keep our audience engaged and informed?
- How can we foster innovation and enable rapid prototyping and delivery, closing the loop between model idea, development, experimentation and production?
- What is the right balance between moving fast now and building a common set of platforms and tools to allow teams to move more quickly in the future?
What You'll Do
- Partner with machine learning engineers on your team to productionalize training, testing, and deployment of new models
- Collaborate with other platform and machine learning 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 quality code in Python and Golang while following best practices in IaC, version control, and continuous delivery
- Use and build upon open-source cloud computing technologies
- Collaborate with data scientists, machine learning engineers, product teams and other key stakeholders and drive ML projects from conception to completion
Who You Are
- You can design and build real time distributed systems for machine learning at scale.
- You are excited about working with machine learning teams to architect and implement tools and infrastructure to turn their ideas into shipped products and features. You're even more excited about finding ways to make all of it faster, easier to understand, more efficient, and more self-service.
- You enjoy keeping up with emerging tech, 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 working with user data in recommendations or other systems comes with a host of privacy and security concerns, and you are both creative and principled in how you approach it.
- You can work independently when needed, and can contribute to multiple team projects simultaneously.
- You have a deep curiosity and are proactive in seeking innovative solutions to business problems.
- You believe in iterating quickly, turning ideas into deployable code. You know the cost of cutting corners and ensure that moving fast does not lead to operational chaos.
- You know when it makes sense to move fast and accrue technical debt. You know when to take the time to do things right from the start and when it is time to address technical debt.
- You are an efficient communicator with your team and collaborators. You know when and why to offer feedback, to get help, or to advocate for your ideas.
- You have collaborated with others on a team to design and build web scale distributed systems.
Things You Should Know
- How to communicate effectively with distributed, remote teams
- How to write robust code in Python, Golang or an equivalent modern programming language
- How to interact with databases, ideally relational
- How to deploy services to a cloud platform
- How to containerize (Docker) and work with container-orchestration systems such as Fargate
- How to work with an IaC tool like Terraform
Things You Might Know
- Prior experience collaborating with data scientists, machine learning engineers, and product stakeholders on machine learning products.
- Commonly used machine learning frameworks (like Keras, PyTorch or TensorFlow) and libraries (like scikit-learn)
- Machine learning tools such as Sagemaker, MLFlow and Metaflow
- Graph databases
- 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
- Common architectures and approaches to creating ML training and inference systems that operate on streams of real-time data rather than batch
- 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!
How We Get Things Done...
This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.
The Legal Bits...
In compliance with local law, we are disclosing the compensation, or a range thereof, for roles in locations where legally required. $129,500.00 - $240,500.00 salary per year. Other rewards may include annual bonuses, short- and long-term incentives, and program-specific awards. In addition, Warner Bros. Discovery provides a variety of benefits to employees, including health insurance coverage, an employee wellness program, life and disability insurance, a retirement savings plan, paid holidays and paid time off (PTO).
Warner Bros. Discovery embraces the opportunity to build a workforce that reflects the diversity of our society and the world around us. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, without regard to race, color, religion, national origin, gender, sexual orientation, gender identity or expression, age, mental or physical disability, and genetic information, marital status, citizenship status, military status, protected veteran status or any other category protected by law.
If you're a qualified candidate with a disability and you need a reasonable accommodation in order to apply for this position, please contact us at [email protected].