Machine Learning Engineer - Growth Teams
Who We Are:
Twitter's Growth teams are dedicated to getting the majority of the world to converse in public using Twitter. We are comprised of many teams across the company, including Product, Engineering, Design, and Data Science. These teams are responsible for understanding the entire user lifecycle, with the goal of helping users discover the value of Twitter, and ultimately paving the way for user acquisition and top line growth.
This mission is to instantly connect people with the conversations and audiences most meaningful to them. Realizing this goal involves work in areas such as machine learning, applied data science, recommendation systems, and information retrieval systems.
Do you want to make a huge impact while working with large data sets at scale? If so, a Growth team is a good fit for you! These high-impact teams value creativity, critical thinking, and teamwork. Consumer growth teams are hiring Machine Learning Engineers in the following areas:
We're hiring for the following teams :
- Onboarding - Building product features to help users onboard, learn how to use Twitter, and consequently hire us to stay informed about what matters to them and join the public conversation.
- Email Recommendations - Detecting the pulse of conversations on Twitter while surfacing the most relevant Events & Topics to users, based on real-time engagement on the platform.
- Notifications - Building a suite of machine learning models to ensure we send relevant & delightful push notifications to our users.
What You'll Do:
- You’ll apply machine learning and/or data science techniques to a variety of modeling and relevance problems involving users, their relationships, their tweets and their interests.
- You will participate in the engineering life-cycle at Twitter, including designing distributed systems, writing production code, conducting code reviews and working alongside our infrastructure and reliability teams.
- Although you will work on cutting-edge problems, this position is not a research position.
Who You Are:
You have a passion for machine learning and improving the ways people consume the world, live. You’re a relevance engineer, applied data scientist or machine-learning engineer who wants to work on exciting algorithmic and deep infrastructure issues. You’re experienced solving large scale relevance problems and comfortable building brand new systems to enable future quality improvements.
- Knowledgeable in one or more of the following: machine-learning, information retrieval, recommendation systems, social network analysis
- Designed and evaluated approaches for handling high-volume real-time data streams.
- A strong technical advocate with a background in Java, C++, or Scala, and Python.
- Comfortable conducting design and code reviews.
- Experienced in operating Linux-based systems.
- Knowledgeable of core CS concepts such as: common data structures and algorithms, profiling/optimization.
- Passionate about working with large unstructured and structured data sets ( for example multi-terabyte+, 100MM+ daily transaction volumes).
- Experienced in collaborating across multiple teams including analytics, product management, and operations.
- B.S., M.S. or Ph.D. in Computer Science or equivalent degree and work experience.
We are committed to an inclusive and diverse Twitter. Twitter is an equal opportunity employer. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any 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.
After you apply, a recruiter may reach out to you for an introductory call.
If your background is a match for the role, you may phone interview with 1-2 people.
If you continue through the process, you will come onsite 1-2 times to interview with a total of 5-10 people.
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