Quantiphi

Artificial Intelligence / Machine Learning Engineering Intern

Quantiphi  •  Bengaluru, IN (Onsite)  •  9 hours ago
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Job Description

Company Description

LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed.

Join us to transform the way the world works.

Job Description

The Artificial Intelligence/Machine Learning team is responsible for protecting LinkedIn members from unprofessional and unsafe content shared on LinkedIn. Using machine learning, computer vision, NLP, data mining, and processing of petabytes of data, we accelerate our mission of making our platform the most trusted professional content network in the world. We are looking for Interns to join this team and solve some of these challenging problems e.g., detection of hate speech, misinformation, and offensive multimedia content.

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is on-site, meaning it will be performed in the specified office on a full-time basis due to the business needs of the team.

Responsibilities

  • Work with BIG data, crunching millions of samples for statistical modeling, data mining, recommendation, or search relevance solutions
  • Write production quality code and influence the next generation of LinkedIn's systems

Qualifications

Basic Qualifications

  • Pursuing B.E/ B.Tech/IDD (Dual Degree)/ M.Tech/MSc in computer science, statistics, mathematics, electrical engineering, machine learning, expected to graduate in 2028
  • Experience with one or more general purpose programming languages: Python, Java, C/C++.

Preferred Qualifications

  • Experience contributing to research communities or efforts, including publishing papers in major conferences or journals and open source project(s).
  • Hands-on experience in one of the areas from Natural Language Processing, Computer Vision, Information Retrieval, Recommender Systems, Machine Learning, Deep Learning, Advanced Statistics and Optimization.

Suggested Skills

  • Ability to design and execute on research agendas.
  • Experience in Deep Learning frameworks(Tensorflow etc.)
  • Experience with Spark, Scala, or other MapReduce paradigms.

Additional Information

India Disability Policy

LinkedIn is an equal employment opportunity employer offering opportunities to all job seekers, including individuals with disabilities. For more information on our equal opportunity policy, please visit https://legal.linkedin.com/content/dam/legal/Policy_India_EqualOppPWD_9-12-2023.pdf

Global Data Privacy Notice and Compliance Posters for Job Candidates

Please use this link to access documents that provide information about how LinkedIn handles the personal data of employees and job applicants, as well as the E-Verify Participation Notice and the Department of Justice Immigrant and Employee Rights Section Right to Work posters: https://www.linkedin.com/legal/candidate-portal.

Quantiphi

About Quantiphi

Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Since its inception in 2013, Quantiphi has solved the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve accelerated and quantifiable business results. Learn more at www.quantiphi.com.

Industry
IT & Software
Company Size
1,001-5,000 employees
Headquarters
Marlborough, Massachusetts
Year Founded
2013
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