Machine Learning Engineer - Ads Targeting & Modeling
Who we are:
Our mission is to provide tools that enable advertisers to reach niche interest groups and personas that resonate with their business. We accomplish this by leveraging machine learning to infer user attributes such as demographics and interests. Advertisers can then use these attributes to specify the type of person their advertisement should target.
What you will do:
Apply the latest machine learning algorithms for image and video classification, tagging, entity resolution, and topic modeling.
Who you are:
You’re a machine learning engineer with an interest in applications to image and video modeling.
BS, MS, or PhD in computer science, data mining, machine learning, information retrieval, recommendation systems, natural language processing, statistics, math, engineering, operations research, or other quantitative discipline; or equivalent work experience
1+ years industry experience
Skilled in using various machine learning algorithms for classification and regression
Fluent in one or more object oriented languages like Java, Scala, C#, C++
Experience with Spark, Hadoop, Pig or other MapReduce-based architectures
Knowledgeable of core CS concepts such as: common data structures and algorithms.
Comfortable conducting design and code reviews.
Experienced in operating Linux-based systems.
Passionate about working with large unstructured and structured data sets
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, genetic information, marital status or any other legally protected status.
San Francisco applicants: Pursuant to the San Francisco Fair Chance Ordinance,
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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