LinkedIn

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.

Team & Role Overview:

The Responsible AI team at LinkedIn serves as the centralized hub of excellence for AI and Data Science, spearheading the technical and organizational strategy to ensure trust, compliance, and safety for all LinkedIn members and clients. We ensure LinkedIn’s AI solutions are aligned with principles of Fairness, Inclusion, Transparency, and now, advanced post-training LLM alignment work. Our work involves fine-tuning and aligning large language models with LinkedIn’s core principles, focusing on critical areas such as privacy, fairness, explainability, safety, hallucination reduction, and robustness.

This team drives applied research and the development of scalable, industry-leading solutions across LinkedIn's AI platforms, models, and products.
 

Location:

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.

This role will be based in Sunnyvale, CA 

Responsibilities:
 

• Conduct independent, hands-on research into the state-of-the-art in differential privacy, secure computation, and privacy-preserving machine learning.

• Evaluate, adapt, and implement advanced algorithmic approaches to optimize for data utility and privacy guarantees in production environments.

• Establish and manage rigorous evaluation frameworks to quantify the fidelity, utility, and privacy guarantees of generated and anonymized data.

• Partner with cross-functional teams of data scientists, software engineers, product managers, and governance specialists to ensure the seamless integration and adoption of new privacy capabilities across the enterprise.

• Develop privacy-first training algorithms and techniques

• Develop evaluation and auditing techniques to measure the privacy of training algorithms

• Design and prototype privacy-preserving machine-learning algorithms (e.g., differential privacy, secure aggregation, federated learning) that can be deployed at enterprise scale.

• Measure and strengthen model robustness against privacy attacks such as membership inference, model inversion, and data memorization leaks—balancing utility with provable guarantees.

• Develop internal libraries, evaluation suites, and documentation that make cutting-edge privacy techniques accessible to engineering and research teams.

• Lead deep-dive investigations into the privacy–performance trade-offs of large models, publishing insights that inform model-training and product-safety decisions.

• Define and codify privacy standards, threat models, and audit procedures that guide the entire ML lifecycle—from dataset curation to post-deployment monitoring.

• Collaborate across Security, Policy, Product, and Legal to translate evolving regulatory requirements into practical technical safeguards and tooling.

• Provide technical leadership and mentorship to a team of engineers, fostering a culture of innovation and excellence.

Basic Qualifications:

• 2+ years as a Technical Lead, Staff Engineer, Principal Engineer, or equivalent.

• 5+ years of overall industry and research experience in AI or Machine Learning Engineering. 

• BA/BS Degree in Computer Science or related technical discipline or equivalent practical experience

Preferred Qualifications:

• 10+ years of overall industry and research experience.

• PhD in Privacy, Security, & Trust or a related discipline. 

• Extensive experience with Differential Privacy, Federated Learning, AI Modeling, and other related solutions. 

• Demonstrated experience effectively collaborating with various teams across the organization including Engineering, Product, Legal/Compliance, etc.  

Suggested Skills:

• Differential Privacy

• AI Privacy 

• AI Modeling

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.

Compensation:

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $198,000 - $326,000. Actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.

The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For additional information, visit: https://careers.linkedin.com/benefits.

Equal Opportunity Statement 

We seek candidates with a wide range of perspectives and backgrounds and we are 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 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.

If you need a reasonable accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us at [email protected] and describe the specific accommodation requested for a disability-related limitation.

Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:

• Documents in alternate formats or read aloud to you
• Having interviews in an accessible location
• Being accompanied by a service dog
• Having a sign language interpreter present for the interview

A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response.

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. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.

San Francisco Fair Chance Ordinance ​

Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.

Pay Transparency Policy Statement ​

As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.

Global Data Privacy Notice for Job Candidates ​

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.

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.

Team & Role Overview:

The Responsible AI team at LinkedIn serves as the centralized hub of excellence for AI and Data Science, spearheading the technical and organizational strategy to ensure trust, compliance, and safety for all LinkedIn members and clients. We ensure LinkedIn’s AI solutions are aligned with principles of Fairness, Inclusion, Transparency, and now, advanced post-training LLM alignment work. Our work involves fine-tuning and aligning large language models with LinkedIn’s core principles, focusing on critical areas such as privacy, fairness, explainability, safety, hallucination reduction, and robustness.

This team drives applied research and the development of scalable, industry-leading solutions across LinkedIn's AI platforms, models, and products.
 

Location:

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.

This role will be based in Sunnyvale, CA 

Responsibilities:
 

• Conduct independent, hands-on research into the state-of-the-art in differential privacy, secure computation, and privacy-preserving machine learning.

• Evaluate, adapt, and implement advanced algorithmic approaches to optimize for data utility and privacy guarantees in production environments.

• Establish and manage rigorous evaluation frameworks to quantify the fidelity, utility, and privacy guarantees of generated and anonymized data.

• Partner with cross-functional teams of data scientists, software engineers, product managers, and governance specialists to ensure the seamless integration and adoption of new privacy capabilities across the enterprise.

• Develop privacy-first training algorithms and techniques

• Develop evaluation and auditing techniques to measure the privacy of training algorithms

• Design and prototype privacy-preserving machine-learning algorithms (e.g., differential privacy, secure aggregation, federated learning) that can be deployed at enterprise scale.

• Measure and strengthen model robustness against privacy attacks such as membership inference, model inversion, and data memorization leaks—balancing utility with provable guarantees.

• Develop internal libraries, evaluation suites, and documentation that make cutting-edge privacy techniques accessible to engineering and research teams.

• Lead deep-dive investigations into the privacy–performance trade-offs of large models, publishing insights that inform model-training and product-safety decisions.

• Define and codify privacy standards, threat models, and audit procedures that guide the entire ML lifecycle—from dataset curation to post-deployment monitoring.

• Collaborate across Security, Policy, Product, and Legal to translate evolving regulatory requirements into practical technical safeguards and tooling.

• Provide technical leadership and mentorship to a team of engineers, fostering a culture of innovation and excellence.

Basic Qualifications:

• At least one year of experience as a Technical Lead or equivalent.

• 4+ years of overall experience in AI or ML Engineering. 

• BA/BS Degree in Computer Science or related technical discipline or equivalent practical experience

Preferred Qualifications:

• 6+ years of overall industry and/or full time research experience.

• PhD in Privacy, Security, & Trust or a related discipline. 

• Extensive experience with Differential Privacy, Federated Learning, AI Modeling, and other related solutions. 

• Demonstrated experience effectively collaborating with various teams across the organization including Engineering, Product, Legal/Compliance, etc.  

Suggested Skills:

• Differential Privacy

• AI Privacy 

• AI Modeling 

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.

Compensation:

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $170,000 - $277,000. Actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.

The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For additional information, visit: https://careers.linkedin.com/benefits.

Equal Opportunity Statement 

We seek candidates with a wide range of perspectives and backgrounds and we are 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 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.

If you need a reasonable accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us at [email protected] and describe the specific accommodation requested for a disability-related limitation.

Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:

• Documents in alternate formats or read aloud to you
• Having interviews in an accessible location
• Being accompanied by a service dog
• Having a sign language interpreter present for the interview

A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response.

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. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.

San Francisco Fair Chance Ordinance ​

Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.

Pay Transparency Policy Statement ​

As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.

Global Data Privacy Notice for Job Candidates ​

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.