AI Researcher

Details

Perplexity is an AI-powered answer engine founded in December 2022. The company focuses on building accurate, trustworthy AI to power decision-making and assistive AI. Perplexity serves hundreds of millions of queries per month and is rapidly scaling its products, including Sonar models, Deep Research Agent, Comet Agent, and Search.

Team Structure

Depending on interests and expertise, you will work on one of three specialized teams:

• Core Research Team (Horizontal): Generate and improve base models that power all products; work on foundational model capabilities, post-training techniques, RL infrastructure, and cross-organization infrastructure.
• Agent Products Team (Vertical): Fine-tune and optimize models for Deep Research Agent and Labs/Canvas; bridge research and product to deliver agent capabilities.
• Comet Agent Team (Vertical): Develop and enhance the Comet Agent product, focusing on product-specific optimizations and requirements.

Responsibilities

Research & Development

• Post-train state-of-the-art LLMs using supervised and reinforcement learning techniques (SFT/DPO/GRPO).
• Leverage a large query/answer dataset to scale model performance across Sonar, Deep Research, Comet, and Search products.
• Stay current with latest LLM research in model training, optimization, and personalization.
• Implement preference optimization and personalization capabilities to improve user experience.
• Invent in-house improvements and optimizations for SOTA models.
• Turn research ideas into algorithms and run experiments to launch new models.

Infrastructure & Implementation

• Own full-stack data, training, and evaluation pipelines required for model development.
• Build robust training frameworks (on top of Megatron/PyTorch) for post-training LLMs.
• Implement infrastructure and components to support cutting-edge model training at scale.
• Integrate models into the product ecosystem.

Collaboration

• Work closely with engineering teams to integrate models into Perplexity's product suite.
• Collaborate across teams to ensure cohesive AI experiences across the platform.
• Partner with product teams to understand user needs and translate them into model improvements.

Requirements

Required

• Proven experience with large-scale LLMs and deep learning systems.
• Strong programming skills in Python and PyTorch; versatility is a plus.
• Experience with post-training techniques and reinforcement learning.
• Self-starter who takes ownership of tasks.
• Passion for tackling challenging problems.
• Minimum 2–6 years of experience on relevant projects (depends on seniority).

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.

Compensation & Benefits

• Cash compensation range: $200,000 - $300,000 per year.
• Equity is part of the total compensation package.
• Benefits include comprehensive health, dental, and vision insurance for you and your dependents, and a 401(k) plan.
• Final offers are determined by factors including experience and expertise and may vary from the amounts listed above.