LinkedIn was built to help professionals achieve more in their careers, and everyday millions of people use our products to make connections, discover opportunities and gain insights. Our global reach means we get to make a direct impact on the world’s workforce in ways no other company can. We are much more than a digital resume – we transform lives through innovative products and technology.
Creating economic opportunity for every member of the global workforce is a responsibility we all share. To truly transform the global economy, we must evolve the way we hire and enable our talent to serve people of all backgrounds and experiences. LinkedIn is committed to diversity in its workforce and is proud to be an equal opportunity employer.
The Search AI team powers some of the most important and visible functionality on LinkedIn. We are the gateway to LinkedIn’s Economic Graph – an interconnected network of millions of people, companies, jobs, groups, events, posts, and more. Our mission is to help members become more productive and successful by helping them easily find who or what they are looking for, make the right connections, and enable meaningful conversations. Our work spans query understanding, query rewriting, machine learned ranking, all the way to state of the art deep learning techniques, neural text modeling, typeahead, and whole page optimization. Search at LinkedIn poses unique challenges in terms of the scale and structure of our graph data, breadth of search use cases and queries posed by our members, need for deep personalization, all while balancing multiple objectives: relevance, downstream value, trustworthiness, and fairness.
You will be responsible for building and deploying relevance models that power our various search verticals, typeahead and blended search systems, keeping scalability and performance in mind through your design and engineering choices. You will explore novel approaches for machine learned ranking, multi-objective optimization, query and user modeling, as well as new ways of leveraging and augmenting training data via active learning, transfer learning, weak supervision, and crowdsourcing. You will work with big data and analyze millions of query logs to understand search patterns and identify opportunities for taking Search to the next level. You will work with partner data science and analytics teams, product, and infrastructure teams to take your ideas from conception to production. You will provide technical leadership to junior engineers in the team, evangelizing and driving the best modeling and engineering practices.
– Bachelor’s degree in Computer Science or related technical field or equivalent technical experience
– Programming experience in Java, C#, or C++
– Knowledge of machine learning and data mining techniques
– 2+ years of industry experience
– 3+ years of relevant work experience in machine learning and large scale software development
– MS or PhD in Computer Science or related technical discipline
– Knowledge of information retrieval, text mining, or natural language processing
– Experience developing, deploying, and debugging machine learning technologies in a production environment
– Experience with Python, Spark, and Hadoop
– Analytical approach and a curious mindset, coupled with solid communication skills
You will Benefit from our Culture:
We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels
Equal Opportunity Statement
LinkedIn is committed to diversity in its workforce and is proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. LinkedIn is an Affirmative Action and Equal Opportunity Employer as described in our equal opportunity statement here: https://lnkd.in/equalemploymentopportunity2017. Please reference http://www1.eeoc.gov/employers/upload/eeoc_self_print_poster.pdf and https://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP_EEO_Supplement_Final_JRF_QA_508c.pdf for more information.
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