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Perplexity

AI Researcher

Advances AI products through post-training SOTA LLMs using supervised and reinforcement learning techniques on rich query datasets. Owns data pipelines, training frameworks, and model integration while collaborating across teams. Requires 2-6+ years in large-scale LLMs and Python/PyTorch expertise; PhD preferred.

220k – 485kSan Francisco, CAPalo Alto, CAAI ResearchOnsite2+ YOE
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About the role

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

Skills

PyTorchPythonLLMsReinforcement LearningSftDpoGrpoMegatronCUDAC++

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