Member of Technical Staff (AI Researcher)

Perplexity; San Francisco (CA); On-site; USD 220,000 - 485,000; Full time

Job summary

Perplexity is seeking top-tier AI Research Scientists and Engineers to advance our AI products and capabilities, including Sonar models, Deep Research Agent, Comet Agent, and Search. You will work on one of three specialized teams and own key research and development tasks—from post-training LLMs to optimization and personalization—driving next-generation experiences.

Collaborate with engineering to deploy training pipelines on Megatron/PyTorch, build scalable infrastructure, and turn ideas into

Qualifications

• Proven experience with large-scale LLMs and deep learning systems.
• Strong Python and PyTorch programming skills.
• Experience with post-training techniques and reinforcement learning.
• Self-starter with ownership of tasks.
• Passion for tackling challenging problems.
• 2–6 years of experience on relevant projects.

Responsibilities

• Develop post-train LLMs using SFT/DPO/GRPO.
• Scale model performance across Sonar, Deep Research, Comet, and Search products.
• Stay current with the latest LLM research, especially in model training, optimization, and personalization techniques.
• Implement preference optimization and personalization capabilities to enhance user experience.
• Invent in-house improvements and optimizations to enhance SOTA models.
• Turn research ideas into algorithms and run experiments to launch new models.

Skills

LLMs

Deep Learning

Python

PyTorch

Reinforcement learning

Post-training techniques

Ownership

Self-starter

Education

PhD in Machine Learning

Tools

Megatron

CUDA

Job description

Perplexity is seeking top-tier AI Research Scientists and Engineers to advance our AI products and capabilities. We're building the future of AI-powered search and agent experiences through our Sonar models, Deep Research Agent, Comet Agent, and Search products. Join us in creating SOTA experiences that handle hundreds of millions of queries and continue to scale rapidly.

Team Structure

Depending on your interests and expertise, you'll work on one of three specialized teams:

• Core Research Team (Horizontal)
• Agent Products Team (Vertical)
• Comet Agent Team (Vertical)

Responsibilities

Research & Development

• Post-train SOTA LLMs using the latest supervised and reinforcement learning techniques (SFT/DPO/GRPO)
• Leverage our rich query/answer dataset to scale model performance across Sonar, Deep Research, Comet, and Search products
• Stay current with the latest LLM research, especially in model training, optimization, and personalization techniques
• Implement preference optimization and personalization capabilities to enhance user experience
• Invent in-house improvements and optimizations to enhance 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 and effective training frameworks (on top of Megatron/PyTorch) for post-training LLMs
• Implement necessary infrastructure and components to support cutting-edge model training at scale
• Integrate models seamlessly into our product ecosystem

Collaboration

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

Qualifications

Required

• Proven experience with large-scale LLMs and Deep Learning systems
• Strong programming skills in Python/PyTorch; versatility is a plus
• 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 (depending on seniority level)

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 Range: $220K - $485K