Staff Machine Learning Engineer – Discovery


Job Overview

Staff Machine Learning Engineer – DiscoveryLocationsSan Francisco, Remote US, SeattleJob descriptionWho We AreTwitter’s Discovery teams are dedicated to getting the majority of the world to converse in public using Twitter. We are composed of many teams across the company, including Product, Engineering, Design, and Research. These teams are responsible for understanding the needs of new users and users who are not very active, and help them discover the value of Twitter by building personalized products.This mission is to instantly connect people with 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 Discovery team is a good fit for you!What You’ll DoYou’ll work with an awesome team of engineers, product managers, data scientists, researchers, and designers to build experiences powered by large-scale recommender systems. This includes:* Collaborating with cross-functional partners to come up with roadmaps for Machine Learning driven products for the team.* Working with product engineers to identify product metrics that causally impact business metrics.* Applying data mining, machine learning, and/or graph analysis techniques to a variety of modeling, relevance, and recommendation problems to build production-quality solutions that balance complexity and performance.* Participating in the engineering life-cycle at Twitter, including designing high-quality ML infrastructure and data pipelines, writing production code, conducting code reviews, and working alongside our infrastructure and reliability teams.* Mentoring other engineers on the team and up-level them on applied product ML skills.Although you will work on groundbreaking problems, this position is not a research position.QualificationsWho You Are* You have strong product understanding and an intuition for how to use modeling to address product needs.* Not only comfortable with ambiguity but view it as an opening to quickly explore a multitude of options.* Apply advanced statistical and machine learning techniques to model user behavior, build benchmark metrics, and drive causal impact using A/B testing.* Have the ability to take on complex problems, learn quickly, iterate, and persist towards a good solution.* Have experience collaborating across cross-functional teams including analytics, product management, and operations.* Help teams come up with roadmaps, prioritize projects based on data and driven execution.* Mentored/coached engineers that apply ML to solve product problems.Requirements* M.S. or PhD in Computer Science (or related field) with 10+ years of relevant industry experience* Experience with one or more of the following: deep learning, reinforcement learning, classification, pattern recognition, recommendation systems, targeting systems, ranking systems or similar.* Experience with data pipelines and large scale data stores.* Firm grasp of CS fundamentals, Data structures, and algorithms* Experience handling large scale quantitative customer data to solve problems and answer questionsAdditional informationWe 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.Engineering hiring processStep 1Once your application is received, a recruiter will reach out pending your qualifications are a match for the role.Step 2If 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.Step 3If the phone interviews go well or your work sample is strong, the final step includes interviews with 5-6 people via a video conference call.

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