Senior Applied Scientist, AI/ML

LinkedIn•Mountain View, CA

10h•$139,000 - $229,000•Hybrid

About The Position

Applied Science teams at LinkedIn empower the company’s products and businesses by driving impact through methodological innovation at scale. We conduct research and build solutions to tackle some of the most challenging business problems across LinkedIn’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 LinkedIn’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 LinkedIn 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 LinkedIn's diverse products. 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.

Requirements

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. R, Python)

Nice To Haves

PhD or Master’s degree in a quantitative field such as Computer Science, Machine Learning, Statistics, Economics, Operations Research, or a related discipline, with a strong research foundation.
MS and 3+ years of relevant work experience, or Ph.D. and 1+ years of relevant work experience developing and deploying machine learning or optimization systems in large-scale, production environments.
Demonstrated experience applying advanced methodologies to real-world marketplace, recommendation, auction, pricing, advertising, or ranking systems or building scalable tooling or platform
Publications in top-tier conferences or journals (e.g., NeurIPS, ICML, KDD, WWW) or equivalent industry research contributions are a plus.
ML/AI System
Statistical Modeling
Platform/System Design
Optimization Techniques (Bandits, RL, Multi-objective Optimization)
LLM/transformer-based Models or Systems
Research

Responsibilities

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.

Benefits

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.
LinkedIn is committed to fair and equitable compensation practices.
The pay range for this role is $139,000.00 to $229,000.00 Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.
The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans.
For more information, visit https://careers.linkedin.com/benefits.
LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities.
Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.
LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant.
As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.
Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.