Member of Technical Staff (ai Researcher)

Perplexity Perplexity · AI Frontier · San Francisco, CA · AI

AI Research Scientist/Engineer at Perplexity focused on advancing AI-powered search and agent experiences. The role involves post-training SOTA LLMs using SFT/DPO/GRPO, leveraging query/answer datasets, and developing in-house improvements. Responsibilities include owning full-stack data, training, and evaluation pipelines, building robust training frameworks, and integrating models into the product suite. Experience with large-scale LLMs, Deep Learning, Python/PyTorch, and post-training techniques is required. Nice-to-haves include PhD, experience with agent systems, and personalization.

What you'd actually do

  1. Post-train SOTA LLMs using the latest supervised and reinforcement learning techniques (SFT/DPO/GRPO)
  2. Leverage our rich query/answer dataset to scale model performance across Sonar, Deep Research, Comet, and Search products
  3. Stay current with the latest LLM research, especially in model training, optimization, and personalization techniques
  4. Own full-stack data, training, and evaluation pipelines required for model development
  5. Build robust and effective training frameworks (on top of Megatron/PyTorch) for post-training LLMs

Skills

Required

  • Python
  • PyTorch
  • post-training techniques
  • reinforcement learning
  • large-scale LLMs
  • Deep Learning systems
  • Python/PyTorch
  • Self-starter
  • take ownership
  • challenging problems

Nice to have

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

What the JD emphasized

  • post-training SOTA LLMs
  • reinforcement learning techniques
  • scale model performance
  • LLM research
  • model training
  • optimization
  • personalization techniques
  • preference optimization
  • personalization capabilities
  • in-house improvements
  • launch new models
  • full-stack data
  • training
  • evaluation pipelines
  • model development
  • training frameworks
  • model training at scale
  • integrate models
  • product ecosystem
  • integrate models into Perplexity's product suite
  • cohesive AI experiences
  • user needs
  • model improvements
  • large-scale LLMs
  • Deep Learning systems
  • Python/PyTorch
  • post-training techniques
  • reinforcement learning
  • Self-starter
  • take ownership
  • challenging problems
  • agent systems
  • multi-step reasoning

Other signals

  • post-training LLMs
  • reinforcement learning
  • large-scale LLMs
  • training pipelines
  • agent systems