Machine Learning Engineer – Search & Agents

About the role

Perplexity is looking for an experienced Machine Learning Engineer to push forward the quality of search and agent systems. You will work across the full stack – from models to agent harnesses and search infrastructure – to create new approaches that improve reasoning, tool use, and task completion.

Key responsibilities

• Advance search and agent quality by improving models, training data, tools, and system design.
• Develop LLM post‑training methods, including reinforcement learning, to enhance reasoning and task execution.
• Train, evaluate, and coordinate multi‑agent systems, building harnesses that manage tools, context, and execution environments.
• Improve retrieval and ranking models and the interfaces agents use to find information.
• Design datasets, reward signals, and evaluations that surface failures and guide improvements.
• Own experiments end‑to‑end, from hypothesis through scalable training, deployment, and measurable gains.

Required profile

• Proven track record of building and shipping ML systems, especially in LLM post‑training, reinforcement learning, search/retrieval, or agent systems.
• Strong software engineering ability across model training, experimentation infrastructure, and production.
• Experience designing rigorous evaluations, diagnosing failures, and turning results into practical improvements.
• Comfort with open‑ended problems that blend research judgment and hands‑on engineering.

Required skills

• Machine learning engineering
• Large language model (LLM) post‑training
• Reinforcement learning
• Search and retrieval systems
• Multi‑agent coordination