About the role

You will own the end-to-end development and deployment of machine learning models, including retrieval, ranking, and classification in large-scale production environments. Additionally, you will collaborate with cross-functional teams to innovate on product features using Generative AI and NLP techniques.

What they look for

Machine Learning Generative AI Recommendation Systems Large Language Models Python Java Scala NLP Statistical Modeling Data Mining Deep Learning Distributed Training Retrieval Ranking Classification Software Engineering

Requirements

Candidates must hold a Master's degree or a Bachelor's degree with at least 5 years of relevant work experience. You are required to have 3+ years of hands-on experience in machine learning, recommendation systems, or related fields, along with proficiency in programming languages like Python, Java, or Scala.

Full 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

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 hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

The Enterprise AI team is responsible for $7B+ lines of business across Hirer, Learning, and Enterprise Jobs. The Hirer AI team connects individuals to opportunities and is building the next-generation recruiter platform with GenAI-powered tools such as AI-assisted search and automated InMails. A new charter, Hiring Assistant, will revolutionize recruiting workflows and is LinkedIn’s #1 priority.

The work in the team includes building, tuning, and applying generative AI models where relevant; working with OpenAI to power GAI applications where relevant; and innovating on cutting-edge search and recommendation models to retrieve the best candidates for a given role. As part of a new and fast-growing team of scientists and engineers, you will experience the excitement and dynamism of a startup along with the scale and technology of a world-class enterprise.

Responsibilities:

Own end-to-end model development and deployment of embeddings, retrieval, ranking, and classification models in large-scale production environments.

Build and deploy relevance models that power recommender systems and personalized intent engines.

Explore novel approaches for ML ranking and downstream impact modeling, and create new product features by leveraging techniques in Generative AI and NLP.

Design with scalability and performance in mind across engineering choices.

Collaborate with partner data science, product, and infrastructure teams to take ideas from conception to production.

Mentor junior ML engineers in applying advanced ML techniques to problems such as entity resolution, lead scoring, and search relevance.

Represent LinkedIn in academic and industry circles by showcasing our innovation, data products, and scientific expertise.

Qualifications

Basic Qualifications

Master’s degree OR Bachelor’s degree with 5+ years of work experience.

3+ years of experience in at least one of the following areas: recommendation systems, machine learning, statistical modeling/inference, data mining, graph/geometric deep learning, large language models, generative AI, or distributed training.

Preferred Qualifications

Ph.D. in computer science, statistics, engineering, mathematics, or a similar quantitative discipline.

3+ years of hands-on experience in recommendation systems, machine learning, statistical modeling/inference, data mining, or graph/geometric deep learning.

Experience with large language models, generative AI, and distributed training.

Understanding of standard programming and software engineering practices.

Proven track record of publishing at academic and industry venues.

Proficiency in programming (e.g., Python, Java, Scala), including production-quality code contributions.

Suggested Skills

Machine Learning

Deep Learning

Generative AI

Large Language Models

Additional Information

India Disability Policy