Member of Technical Staff (AI Researcher)

The role

You will research and develop SOTA LLMs using supervised and reinforcement learning techniques. You will work on one of three teams focusing on core research, agent products, or the Comet Agent product.

What you will do

• Post-train SOTA LLMs using SFT, DPO, and GRPO
• Leverage query/answer datasets to scale model performance
• Stay current with LLM research in training, optimization, and personalization
• Implement preference optimization and personalization capabilities
• Invent in-house improvements and optimizations for SOTA models
• Turn research ideas into algorithms and run experiments
• Own full-stack data, training, and evaluation pipelines
• Build training frameworks on top of Megatron/PyTorch
• Implement infrastructure to support model training at scale
• Integrate models into the product ecosystem

Required qualifications

• Proven experience with large-scale LLMs and Deep Learning systems
• Strong programming skills in Python/PyTorch
• Experience with post-training techniques and reinforcement learning
• Self-starter with a willingness to take ownership of tasks
• Passion for tackling challenging problems
• Minimum 2-6 years of experience on relevant projects

Nice to have

• PhD in Machine Learning, AI, Systems, or related areas
• Experience in post-training LLMs with SFT/DPO/GRPO
• C++/CUDA programming skills
• Experience building LLM training frameworks
• Academic publications and research impact
• Experience with agent systems and multi-step reasoning
• Background in personalization and preference learning

About the company

Perplexity is building the future of AI-powered search and agent experiences through Sonar models, Deep Research Agent, Comet Agent, and Search products.