Twitter

Sr. ML Engineering Manager - Recommended Notifications

Twitter

March 26, 2021

San Francisco, CA 94103, US

Company Description
Twitter is what’s happening and what people are talking about right now. For us, life's not about a job, it's about purpose. We believe real change starts with conversation. Here, your voice matters. Come as you are and together we'll do what's right (not what's easy) to serve the public conversation.
Job Description
Twitter is looking for an engineering leader to oversee the relevance team for the Notifications Relevance team. The team's mission is to make sure that Twitter customers never miss the things they care about most. We build relevance and machine learning models and systems to power the core of the Twitter notifications product. Our systems evaluate candidates from nearly half a billion daily tweets to select, organize, and deliver the most personalized content to our users. The recent products and technologies built by our team have shown consistent results in driving new active users and long-term retention and are some of the largest contributors to audience growth on the platform.
What You'll do: You will join the Notifications Relevance team and lead a world-class team of Machine Learning engineers. We’re looking for a hands-on, technical manager with a passion for working on customer-facing relevance products. The ideal candidate would be equally comfortable guiding the long-term roadmap of the modeling team and work with the product engineering team to identify new product relevance problems.
Qualifications

As a Manager for the Notification relevance team you will:

  • Mentor the professional development of each direct report through personal and performance management.
  • Working with your Tech lead, take responsibility for the group’s technical strategy and roadmap – creating success metrics, to measure and evaluate the performance of models and understand levers of model performance
  • Work with your Product, Data Science, and EM partners to understand and incorporate customer problems into the team’s roadmap, propose ML-based solutions to customer needs and align priorities with our overall product strategy.
  • Seek diverse perspectives to drive bottom-up innovation and create consensus from all technical partners inside and outside the team ( applied research teams).
  • Ensure the team fully understands the goals and objectives of Twitter as a company and how their work fits into the bigger picture.

Who You Are:
  • You have a background in machine learning, including experience with deep learning, prediction/binary classification, and decision trees. Experience with recommender systems is a plus.
  • You have experience with A/B experimentation best practices and defining key metrics.
  • You can hold your own technically with engineers on the team and give constructive feedback on projects and ideas
  • You have a sense of urgency, move quickly, and ship things
  • You support giving engineers the tools, confidence, and motivation they need to make decisions independently that lead to the recognition of your engineers and not just yourself.
  • You are a strong recruiter of engineering talent and comfortable closing applicants for your team and the business

Requirements
  • BA/BS or higher in Computer Science (or related field)
  • Previously tech-led or managed a team of 5 or more engineers building ML models and systems in a production setting
  • Knowledge of and experience with techniques used in data mining, machine learning, information retrieval, recommendation systems, or natural language processing.

Additional Information

We are committed to an inclusive and diverse Twitter. Twitter is an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran status, genetic information, marital status or any other legally protected status.

San Francisco applicants: Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.