Senior AI Engineering Manager, Enterprise AI

Company Description

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.

Job Description

Team Overview:

The Hirer AI team is building the LinkedIn Hiring Assistant, which is an AI agent that autonomously finds job candidates and writes messages to them, and is a key initiative for the company. Our team works on a wide range of cutting-edge ML: LLM fine tuning, text generation, LLM-as-a-judge, prompt engineering, embedding-based retrieval, and search ranking. As a part of the Hirer AI team, you will engage with highly experienced and passionate AI Engineers and a brilliant cross functional team to help millions of LinkedIn users connect with the best possible opportunities.

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.

Key Responsibilities

As a senior AI Manager, you will lead a team of applied scientists and engineers to design and deliver scalable LLM and matching solutions that improve the relevance and quality of LinkedIn’s Talent Marketplace. You’ll combine people leadership, technical vision, and cross-functional collaboration to maximize team impact.
 

• Build and grow talent: Attract, mentor, and develop a diverse and high-performing team of applied scientists and engineers. Foster an inclusive culture where people feel empowered to share ideas, take intelligent risks, and grow into technical leaders.

• Define clear direction: Translate product and business needs into a focused technical roadmap. Connect daily team work to LinkedIn’s broader mission and long-term strategic priorities.

• Advance LLM and matching systems: Guide the team in developing and improving hybrid retrieval systems, and domain-tuned LLM applications. Leverage LinkedIn’s unique data to ground model outputs, enhance job matching relevance, and improve end-to-end quality.

• Ensure scalability and efficiency: Partner with infra and platform teams to optimize retrieval and serving efficiency, including embedding optimization, adaptive caching, and parameter-efficient fine-tuning.

• Promote alignment and collaboration: Work across teams to identify shared opportunities, align on long-term goals, and drive consistent progress. Proactively address misalignments with clarity and empathy.

• Foster innovation and inclusion: Create an environment that values curiosity, diverse perspectives, and open dialogue. Encourage the team to challenge assumptions, experiment responsibly, and continuously raise the technical bar.

Qualifications

Basic Qualifications:

• 3+ years of management experience, leading team(s) of direct reports.

• 7+ years of relevant professional experience in AI/ML Engineering and/or leadership of AI/ML Engineering teams. 

• Bachelors in Computer Science or other technical discipline, or related practical technical experience

Preferred Qualifications
 

• 10+ years of relevant professional experience in AI, Machine Learning, or a related field. 

• Prior experience leading teams of 15-20+ individuals, including multiple Tech Leads. 

• PhD in Computer Science, Machine Learning, Statistics or related fields

• Experience leading teams in machine learning, NLP, or retrieval systems.

• Strong technical background in LLMs, retrieval architectures, and relevance optimization.

• Familiarity with semantic search, vector databases, or efficient model adaptation.

• Proven ability to connect technical strategy to business goals and communicate across disciplines.

• Passion for developing talent and building highly collaborative teams.
   

Suggested Skills:

• Team Management 

• AI System Design (Recommendation, Relevance, and R