AI Engineer, Applied ML

at Perplexity AI
📍 New York City, United States
📍 Palo Alto, United States
📍 San Francisco, United States
USD 200,000-300,000 per year
MIDDLE
✅ Hybrid

SCRAPED

Used Tools & Technologies

Not specified

Required Skills & Competences ?

Python @ 6 A/B Testing @ 3 Algorithms @ 3 TensorFlow @ 3 Data Analysis @ 3 NLP @ 3 LLM @ 3 PyTorch @ 3

Details

Perplexity is looking for an Applied ML Engineer to design, build, and iterate on cutting-edge AI models powering the core experience. You will develop scalable and impactful solutions for user personalization, query understanding, and content discovery used by millions of users.

Responsibilities

  • Apply state-of-the-art ML and LLM techniques to problems including:
    • 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; design experiments and metrics to measure 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 AI drives meaningful product improvements.
  • Stay current with ML/AI research and incorporate emerging algorithms into the product lifecycle.

Requirements

  • 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; able to excel in high-velocity, cross-functional teams.
  • BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience).

Bonus Points

  • Experience with LLM prompt engineering and Retrieval-Augmented Generation (RAG) systems.
  • Experience with large-scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking).
  • Open-source or published contributions in ML, NLP, IR, or related research fields.

Compensation & Location

  • Base salary range: $200K–$300K. Offers equity.
  • Workplace: Hybrid (San Francisco). Additional locations: New York City; Palo Alto.