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PerplexityPerplexitySan Francisco, CA

AI Engineer, Applied ML

Designs, builds, and iterates on AI/ML models for personalization, query understanding, and content discovery. Requires 5+ years in ML, deep learning expertise (PyTorch/TensorFlow/JAX), Python, and full ML lifecycle ownership.

220k – 405k/yr
On-site5+ YOEML Engineering

About the role

Key Responsibilities

  • Apply state-of-the-art ML and LLM techniques to solve problems spanning:
    • Personalization (LLM memory, context summarization, retrieval and ranking)
    • Query Understanding (intent modeling, rewriting, agentic decomposition)
    • Content Discovery (feed ranking and surfacing)
  • Rigorously evaluate LLM/ML models with both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact
  • Own the entire model lifecycle from research to production: data analysis, modeling, evaluation, offline/online A/B testing, and iterative improvement
  • Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements
  • Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle

Preferred Qualifications

  • 5+ years experience building and shipping robust ML/AI models for large-scale, user-facing or data-driven products
  • Deep expertise in deep learning (PyTorch, TensorFlow, JAX), LLMs, information retrieval, content summarization, recommendation systems, NLP, and/or ranking
  • Strong software engineering skills (Python, production-quality codebases, collaborative development)
  • In-depth experience with the full ML lifecycle: data analysis, feature engineering, iterative model development, rigorous evaluation, and ongoing monitoring/improvement
  • Proven collaborator and communicator; excels in high-velocity, cross-functional teams
  • Curious, driven by end-user/product impact, and passionate about advancing the state of applied ML and AI
  • BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience)

Bonus Points For

  • Experience with LLM prompt engineering, Retrieval-augmented generation (RAG) based systems
  • Experience in large scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking, etc)
  • Open-source or published contributions in ML, NLP, IR, or relevant research fields

Skills

PyTorchTensorFlowJAXPythonLLMsNLPInformation RetrievalRecommendation SystemsRAG

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