Senior Applied Scientist, AI​/ML

Company Overview

Linked In 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

Applied Science teams at Linked In empower the company’s products and businesses by driving impact through methodological innovation  conduct research and build solutions to tackle some of the most challenging business problems across Linked In’s ecosystem—from market and system design (e.g., auctions, matching), to optimization and personalization. Composed of scientists passionate about translating theory into practice, these teams build scalable, production-ready solutions (e.g., models, tools and platforms) that support product, customer value, and long-term business growth.

As a Senior Applied Scientist, you will play a critical role in advancing Linked In’s next generation of AI-powered systems by leading methodological research in areas such as prediction, measurement, and optimization, and transforming research into scalable, production-grade models, tools and platforms. In this role, you will drive cutting-edge R&D and integrate advanced methodologies into end-to-end systems that operate across multiple Linked In products and surfaces.

You will design and develop novel methods and models, and deploy large-scale ML / DL / RL / LLM solutions that are reliable, efficient, and maintainable in production. You will transform research solutions into robust, reusable tooling and platforms, and work on problems to boost the growth of Linked In’s diverse products.

At Linked In, 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 Linked In office on select days, as determined by the business needs of the team.

• Identify and shape new opportunities where advances in methodology, AI, or system design can unlock step-function improvements to marketplace performance and platform health.
• Lead methodological research in optimization or measurement, for large-scale, multi-sided marketplaces, including auction design, matching mechanisms, and personalization.
• Translate research into business impact by transforming novel methodologies into scalable, reliable, and reusable production-grade models, tools, and platforms.
• Design and develop advanced models (ML, DL, RL, LLM-based systems) to solve prediction, causal measurement, and optimization problems that directly impact marketplace efficiency, revenue, and member experience.
• Own end-to-end system design and deployment, from problem formulation, prototyping, modeling and experimentation in production environments.
• Drive cross-functional collaboration with Engineering, Product, and cross-functional partners to align on problem definitions, trade-offs, and execution plans, and to ensure successful adoption of scientific solutions.
• Contribute to technical direction and best practices for the Applied Science team, influencing modeling standards, production craftsmanship and operational excellence.
• Mentor and elevate junior scientists, providing technical guidance, design reviews, and thought leadership to raise the overall bar for research quality and impact.

Basic Qualifications

• Bachelor’s Degree in a quantitative discipline:
  Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.
• 3 years of industry or relevant academia experience
• Background in at least one programming language (eg. R, Python, Java, Ruby, Scala/Spark or Perl)
• Experience in applied statistics and statistical modeling in at least one statistical software package, (eg.…