Company description

In Twitter, we serve billions of ad impressions and generate millions of dollars in revenue per day. Behind every ad, our ads prediction system, which is one of the largest ML services at Twitter, evaluates at least thousands of candidates behind the scenes. Our mission is to leverage state of the art machine learning and data science techniques to allow advertisers effectively reach hundred millions of twitter users who resonate with their business, in a way that protects the integrity of their brand.

This includes applying machine learning techniques to both user modeling and content modeling: examples include inferring user demographics and interests, predicting the probability a user will engage with an ad, and topic modeling for mixed-media containing text, image, and videos! When executed successfully, we create aha! moments for our users & advertisers and add huge value to the Twitter business & revenue.

Job description

• Apply machine learning and data mining techniques for a variety of modeling and relevance problems involving users, their tweets, their interests, Twitter ads, relationship among entities. Be a key contributor to Twitter’s continued use of cutting edge machine learning in all aspects of our solutions. 
• Implement and adapt state-of-the-art ML methods to scalable, efficient, and performant production systems. 
• Run meaningful and reproducible experiments and perform critical analysis / interpretation of results.
• Conduct a literature review, construct context for the proposed problem and critique prior work within the right context for Twitter.
• Work cross functionally with our product management team to build new solutions for Advertisers, and prove via experimentation that they deliver value to them 

Qualifications

Who you are:

You're someone with a track record of applying Machine Learning to production systems and know how to strike a right balance between software engineering and research for a high impact, You now are looking to grow with and help impact a global at scale business. The prospect of getting to work with an open-source tech stack that powers a solution people around the world love to use, and where massive volumes of data are extracted in real time excites you. You want to mentor amazing engineers; but you are also learning and want to see continued investment in you by the business; and you expect excellence in everything. You see your future self as a force multiplier and not just a great technical resource for an employer. You see challenge as an opportunity and you're looking to jump ahead of the pack in your career. You're applying to this role because you're hoping for a chance to jump in and see what you can do to make a difference in an iconic software company.

Qualification:

• 3+ years of industry experience using machine learning to solve real-world problems with large datasets (multi-terabyte+, 100MM+ daily transaction volumes)
• A graduate degree in artificial intelligence, machine learning or equivalent experience
• Fluent in one or more object oriented languages like Java, Scala, C#, C++
• Knowledgeable of core CS concepts such as data structures and algorithms
• Experience with standard software engineering practices (e.g. unit testing, code reviews, design documentation)
• Extensive experience building scalable machine learning systems and data-driven products working with cross functional teams
• Experience with handling large scale data using MapReduce-based architectures such as Scalding, Pig, Hive
• Experience with adtech ecosystem is a plus

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, 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.