WW is looking for candidates to help change people’s lives. We are a global wellness technology company inspiring millions of people to adopt healthy habits for real life. We do this through engaging digital experiences, face-to-face workshops and sustainable programs that encourage people to move more, shift their mindset and eat healthier while enjoying the foods they love. By drawing on over six decades of experience and expertise in behavioral science, we build communities in order to deliver wellness for all.
Who we are:
WeightWatchers is inherently a data product and we can leverage our data -- a massive longitudinal dataset across a wealth of touch points and millions of members in different geographies and demographics. This is what helps us to create a profoundly personalized and impactful experience. The Machine Learning Engineering team is a separate entity from the Analytics, Data Warehousing and Data Engineering teams, freeing us up for a singular focus: building awesome data products.
What you will do:
The WeightWatchers Machine Learning Engineering team is looking for a new senior member to help us build the infrastructure that powers our predictive models, recommenders, and data enrichment algorithms. In your role you will help WeightWatcher's Data Science team to develop, deploy, monitor, and maintain production machine learning (ML) models, including serving LLMs. You will build out and monitor CI/CD pipelines for automated image builds, running of tests, and deployment of models using e.g. GitHub Actions. You will be evaluating existing ML processes to identify areas of improvement, and will apply the latest open source technologies in the ML stack to enhance WeightWatcher's predictive automation software.
Who You Are:
Minimum of 5 years of experience in a Production Operations environment
Strong production level software experience and deep knowledge and understanding of machine learning principals
Expert in Python
Expert in SQL
Strong understanding and use of Kubernetes as well as the ins-and-outs of containerization
Experience with Docker
Machine learning product development experience, using state-of-the-art tooling
Ability to create abstractions, APIs, and libraries
Extensive knowledge of ML frameworks/libraries (e.g. scikit-learn, Pytorch, Tensorflow, transformers), data structures, data modeling, and software architecture within a cloud production environment
Experience working with hosted LLM providers, serving self-hosted LLMs, fine-tuning, prompt engineering, and the quickly evolving tooling used to power gen-AI applications
WW practices a member-first mindset and prioritizes initiatives based on impact. If you thrive in collaborative settings, appreciate the opportunity to operate at different altitudes, lead from the front, and believe in results-based accountability, we would love to talk to you.
Our titles cover more than one career level. The starting range for this role is $150,000 to $180,000 a year. Actual base pay may vary depending on, but not limited to: skills, education and years of experience. This role is also eligible for a comprehensive benefits package and annual bonus program.
If you have questions or would like to submit a referral, please reach out to Eric Chan at Eric.Chan@ww.com
At WW, it is our priority to cultivate a diverse and inclusive workplace. We are committed as individuals, as an organization, and as fellow humans, to advocate for and support our employees, our members, and our communities. We are proud to be an equal opportunity employer and we do not discriminate on the basis of sex, race, color, creed, national origin, marital status, age, religion, sexual orientation, gender identity, gender expression, veteran status, or disability.
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