AI Research Lead

Details

Perplexity is an AI-powered answer engine founded in December 2022. The company has raised significant venture investment and aims to build accurate, trustworthy AI to power decision-making. Perplexity serves hundreds of millions of queries per month and is scaling its in-house Online LLMs, the Sonar models.

Role overview

We are seeking an exceptional AI Research Tech Lead to drive research strategy and lead development of our Sonar Online LLMs. In this leadership role you will set macro research direction across modalities, mentor a team of researchers, and leverage Perplexity's query/answer dataset to scale Sonar model performance and deliver a state-of-the-art online LLM experience.

Responsibilities

• Define and execute macro research direction across multiple modalities, including post-training LLMs for agent trajectories and future mid-training initiatives.
• Lead strategic research planning and roadmap development to advance Sonar model capabilities.
• Drive innovation in supervised and reinforcement learning techniques for query answering.
• Own and optimize the full stack data, training, and evaluation pipelines required for LLM post-training.
• Post-train state-of-the-art LLMs on query answering using cutting-edge supervised and reinforcement learning techniques.
• Drive research into agent trajectories and multi-modal capabilities and lead technical roadmap for eventual mid-training investments.
• Coach, mentor, and grow a team of AI research scientists and engineers; lead hiring and onboarding of new research talent.
• Collaborate cross-functionally with engineering, product, and data teams to integrate Sonar models into product, translate user needs into research priorities, and leverage the query/answer dataset effectively.
• Communicate research progress and findings to stakeholders across the organization.

Requirements

• Minimum of 5 years working on relevant AI/ML projects, with 3+ years in a technical leadership role.
• Proven track record of leading and mentoring technical and research teams.
• Computer Science graduate degree from a premier academic institution.
• Deep expertise with large-scale LLMs and deep learning systems.
• Strong programming skills and versatility across multiple languages and frameworks.
• Demonstrated ability to set technical vision and drive execution.
• Experience with pre-training and post-training techniques, including self-supervised learning and approaches referenced as SFT/DPO/GRPO/PPO.
• Self-starter with strong ownership mentality and ability to operate in ambiguous environments.
• Passion for solving challenging problems and advancing AI research.

Nice-to-have

• PhD in Machine Learning, Computer Science, or related areas.
• Experience with agent-based AI systems and multi-modal model development.
• Background in mid-training or large-scale pre-training of LLMs.
• Publications in top-tier AI/ML conferences.
• Experience in fast-paced startup environments and a track record of translating research into production systems.

Compensation & Benefits

• Cash compensation range: $370,000 - $460,000.
• Final offer amounts depend on experience and expertise and may vary from the listed range.
• Equity may be part of the total compensation package.
• Benefits include comprehensive health, dental, and vision insurance and a 401(k) plan.