Machine Learning Engineer - Connect
Twitter's Connect Team is responsible for core features of twitter.com, which includes Search, Recommendations, Events, and more! Our code operates at massive scale and speed, serving billions of requests per day, connecting hundreds of millions of active Twitter users to real-time information about their lives and the world we live in.
Who We Are:
At Twitter, our mission is to instantly connect users to the information most meaningful to them. Realizing this involves work in areas such as machine learning, applied data science, recommendation systems, information retrieval systems, natural language processing, large graph analysis, spam, etc.
Do you want to make a huge impact while working with large data sets at a really big scale? If so, this might be a good fit for you! Consumer Product teams are hiring Machine Learning Engineers in the following areas:
- Recommendations - Combining real time relevance, richer timelines, and recommendations, to put the most interesting and relevant content in front of our users at all times.
- Event 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.
- Search Quality - Building the real-time search engine for Twitter, finding, personalizing and organizing relevant content for users. You will create algorithms to understand the users' intent through query and context, to rank and organize content, and to extract insights to make suggestion, to help user better navigate content on Twitter, by solving a wide range of problems in IR/NLP/ML, etc.
- 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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