💼 Summer 2026 at LinkedIn as an AI/ML Engineer Intern

Mon, 31 Aug 2026 00:00:00 +0000

This summer I joined LinkedIn in Mountain View, CA as an AI/ML Engineer intern on the Generative AI team, working on candidate retrieval for LinkedIn’s hiring assistant.

What I worked on

Generative retrieval for hiring. I developed generative AI models for candidate retrieval, improving hiring recall by 46.4% and role fit by 33.3% in offline evaluation through reinforcement learning.
The team’s first RL pipeline. I built the group’s first reinforcement-learning pipeline for candidate retrieval, using LLM-based rewards. To keep training fast, I created a candidate-profile lookup table that replaced per-step online requests and cut training latency.
Constrained decoding. I implemented constrained decoding to prioritize candidates who are actively seeking jobs, increasing their share among retrieved candidates.
Codebook evaluation. Alongside the retrieval work, I built an end-to-end pipeline for assessing training codebook quality for generative recruiting models — data checks, drift detection, and robustness analyses across cohorts and over time — and proposed a task-specific metric aligned to downstream hiring objectives such as match relevance and conversion propensity.

What I took away

Coming from a research background in LLM safety and unlearning, the biggest shift was how much of production ML is about evaluation infrastructure rather than modeling. A reward model is only as trustworthy as the offline evaluation you check it against, and most of the leverage came from making that evaluation faster and more honest.

Thanks to my mentor and the whole team for a great summer.

Internship |

💼 Summer 2026 at LinkedIn as an AI/ML Engineer Intern

What I worked on

What I took away