Manager, Machine Learning + Software Engineering

LinkedIn is the worlds 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. Were also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture thats built on trust, care, inclusion, and fun where everyone can succeed

LinkedIn Marketing Solutions (LMS) helps B2B brands reach, engage, and convert professional audiences on a safe, trusted platform. The Ads Trust Engineering charter builds scalable AI systems that improve Ads auto-review, traffic quality, brand safety/suitability/viewability, and transparency for members and advertisers, while enabling partner-led growth.

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.

As a Manager, you will lead end-to-end ML systems and AI defenses that protect members and maximize advertiser ROI across LinkedIn Marketing Solutions. You’ll own architecture and delivery from data pipelines to low-latency inference, partner across product/infra/DS to set technical direction, and raise the bar on engineering craftsmanship and operational excellence.

Responsibilities:

• As a manager, you will participate in key technical and design discussions with technical leads in the team. 

• You will lead the AI strategy for an area and work with the engineering and product leads to align it with the larger vision for the LinkedIn Ads ecosystem. 

• You will collaborate with application engineering, product, and partner teams to design machine learning solutions to power LMS products. 

• You will attract world class talent and provide technical guidance, career development, and mentoring to team members. 

• You will hold the team to a high-quality bar for machine learning tech and actively reduce tech debt, designing systems that scale with algorithms and with time, and improve engineering productivity. 

• You will partner with data science & analytics teams, product & infrastructure teams to build and onboard new modelling use cases online.

Basic Qualifications

• Engineering degree in Computer Science or related field, or equivalent practical experience.

• MS/PhD degree in Computer Science, Information Retrieval, Machine Learning, Natural Language Processing, or a related discipline.

• 8+ years of experience in machine learning, artificial intelligence, or related fields.

• 5+ years in an architect or technical leadership role driving large-scale AI initiatives.

• Hands-on experience with end-to-end ML lifecycle: data pipelines, training, deployment, monitoring, and optimization.

• Experience of leading a team of engineers as a people manager.

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

Preferred Qualifications

• 3+ years of management experience.

• MS or PhD in Computer Science, Statistics or related technical discipline

• Experience with machine learning, optimization algorithms, and/or deep-learning techniques

• Experience developing large scale systems

• Leadership or mentorship experience is preferred

• Analytical approach coupled with solid communication skills and a sense of ownership

• Track record of producing papers in conferences such as KDD, ... and Patenting Innovations

• Experience with LLMs/GenAI (prompt design, evaluation, safety) and applying them to production trust/safety or relevance problems.

• Expertise in streaming frameworks (e.g., Flink/Samza) and large-scale storage/indexing; designing resilient caches and partition strategies.

• Background in ad tech (Ads review, auction dynamics, measurement, viewability, brand safety/suitability, IVT) and working with external partners/signals.

• Familiarity with privacy, compliance, and transparency domains within digital advertising.

Suggested Skills

• Machine Learning, GenAI/LLMs 

• Distributed Systems & System Design

• Streaming Data 

• Experimentation, Observability

• AdTech Domain Expertise

 

You Will Benefit From Our Culture

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