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Perplexity AI is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and work together to solve complex problems. The role spans search and retrieval, LLM post-training, multi-agent training, and the harnesses that make these systems effective.
Perplexity controls the full stack: the models, the agent harnesses, and the search infrastructure. This freedom enables development of new approaches across all three—training models to use search more effectively, designing tools and execution environments around learned behavior, and improving retrieval to support how agents actually work.
Key responsibilities include:
- Push search and agent quality forward through improvements to models, training data, tools, and system design
- Develop LLM post-training methods, including reinforcement learning, to improve reasoning, search, tool use, and task completion
- Train and evaluate multi-agent systems, exploring how agents divide work, share information, and coordinate effectively
- Design and build agent harnesses: the tools, context management, execution environments, and orchestration that support reliable work over many steps
- Improve retrieval and ranking models and the search interfaces agents use to find and assess information
- Build datasets, reward signals, and evaluations that expose meaningful failures and guide improvements
- Own experiments end to end, from hypothesis to scalable training, deployment, and measurable gains in quality, latency, and cost
- Collaborate with AI, Search, Infrastructure, Data, and Product teams to bring new capabilities into production
Qualifications:
- Strong track record of building and shipping ML systems, with deep experience in one or more of: LLM post-training, reinforcement learning, search and retrieval, or agent systems
- Strong software engineering skills and ability to work across model training, experimentation infrastructure, and production systems
- Experience designing rigorous evaluations, diagnosing failures, and translating experimental results into practical improvements
- Comfort with open-ended problems requiring both research judgment and hands-on engineering
- Strong sense of ownership, curiosity, and drive to carry ideas through to working systems
Relevant experience (preferred):
- Training models to use tools or complete tasks over many steps
- Multi-agent training, coordination, or evaluation
- Building agent harnesses, distributed training systems, or scalable inference infrastructure
- Large-scale retrieval, ranking, or recommendation systems
The company values depth in a relevant area and the ability to learn across the stack; prior expertise in every area is not required.
About Perplexity AI
AI / Data / Infrastructure — AI answer engine and search product for consumers and enterprises.
