Machine Learning Engineer - Consumer Product
Consumer Products is responsible for core features of twitter.com, which includes timelines, tweets, search, trends, recommendations, tweet details/permalink, 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 recommendation systems, information retrieval systems, natural language processing, large graph analysis, applied data science, machine learning, spam, etc. Do you love Twitter? 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 is hiring for 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.
Abuse Detection- Through a mix of user-facing features and automated detection, we work to make Twitter a safe platform for everyone to share their views with the world.
Search Quality - Responsible for all real-time search on Twitter whether that’s for tweets, users, news, or videos.
Trends - Detecting the pulse of conversations on Twitter while surfacing the most relevant topics to users, based on real-time engagement on the platform.
- Timelines Quality - Applying ranking, relevancy, and machine learning to the home timeline. Finding and choosing the best and most relevant Tweets to show to users.
Although you will work on cutting-edge problems, this position is not a research position. 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. You’ll apply data science, machine learning and/or graph analysis techniques to a variety of modeling and relevance problems involving users, their relationships, their tweets and their interests.
Who You Are:
You’re an 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 doing incremental quality work while 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 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.
Engineering Hiring Process
Once your application is received, a recruiter will reach out pending your qualifications are a match for the role.
If your background is a match, you may have 1-2 technical phone interviews or be given the chance to provide a work sample depending on the role.
If the phone interviews go well or your work sample is strong, the final step includes interviews with 5-6 people held onsite in our office.
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