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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.

This role will be based in Mountain View, CA. 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.

We are seeking an Engineering Manager to lead the Marketing Solutions Marketing Science team, a team responsible for casual measurement of Ads performance. As the Engineering Manager, you'll spearhead the development and optimization of our next-generation Incrementality and A/B Testing Advertiser products and technologies to prove advertiser spend on LinkedIn and guide advertisers towards stronger incremental results.

The ideal candidate will be excited for the challenge to transform and think critically on casual measurement approaches for B2B Advertising. With an experience in experimentation, analytical products and data modeling the candidate will disrupt how B2B Advertisers prove the value of investing in LinkedIn Ads.

Responsibilities

+ Innovation Leadership: drive advancements in Incrementality Measurement and Optimization and simplifying system architecture for improved performance and scalability. Define and productionalize new approaches for casual proof of marketing performance to LinkedIn clients.

+ Value Delivery: Implement tools and processes that prove incremental ROI for advertisers by spending on LinkedIn. Create actionable insights and recommendations for advertisers to improve ROI and incremental value of spend on LinkedIn. Partner with the optimization team to optimize, deliver and validate incremental value created.

+ Operations: Scale and enhance core experimentation infrastructure meet the requirement in scale, statistical accuracy and reliability. Guide the team in scaling distributed systems, making architectural trade-offs, and applying synchronous/asynchronous design principles.

+ Team Empowerment: Build and mentor a high-performing engineering team capable of addressing challenges like marketplace balance, system reliability, and modern monetization strategies. Lead and grow a team of 10 full stack engineers, supporting their performance, career development, and technical growth. Act as a role model and coach, fostering a culture of high integrity, craftsmanship, and continuous improvement.

+ Partnership: Directly drive technical collaboration with external incrementality and A/B Testing measurement providers, and large LinkedIn clients to ensure valid and provable incremental marketing and sales outcomes are created by marketing spend on LinkedIn.

+ Strategy: Collaborate with senior leadership and XFN teams to define and evolve the long-term technology vision and roadmap.

Basic Qualifications

+ BA/BS Degree in Computer Science or related technical discipline, or equivalent practical experience.

+ 1+ year(s) of management experience or 1+ year(s) of staff level engineering experience with management training

+ 5+ years of industry experience in software design, development, and large-scale software engineering

+ Experience programming in object-oriented and/or functional programming languages (like Java/Scala/Python/C/C++/etc.)

Preferred Qualifications

+ MS or PhD in Applied Math, Computer Science, Statistics or related technical discipline

+ 2+ years of hands-on software engineering/technical management and people management experience

+ 7+ years industry experience in software design, development, and algorithm related solutions.

+ 5+ years programming experience in languages such as Java, Scala, C/C++, C#, Python, Go, etc.

+ 3+ years of experience building Ads Measurement or Analytical products

+ 3+ years in experience building offline / data processing systems

+ 2+ years of experience driving external client and partners measurement or advanced analytics programs.

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.

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $147,000 - $240,000. 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.