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описание
Perplexity develops AI systems for search, reasoning, and collaborative problem-solving. Its work spans search and retrieval, LLM post-training, multi-agent training, and the infrastructure supporting these systems.
задачи
Improve search and agent quality through 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
Design and build agent harnesses, including tools, context management, execution environments, and orchestration for reliable multi-step work
Improve retrieval and ranking models and the search interfaces agents use to find and assess information
Build datasets, reward signals, and evaluations that expose failures and guide improvements
Own experiments end to end, from hypothesis through scalable training, deployment, and measurable improvements in quality, latency, and cost
Collaborate with AI, Search, Infrastructure, Data, and Product teams to bring capabilities into production
требования
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
Comfortable with open-ended problems requiring research judgment and hands-on engineering
Strong ownership, curiosity, and drive to take ideas through to working systems
Будет плюсом: training models to use tools or complete multi-step tasks, multi-agent training, coordination or evaluation, agent harnesses, distributed training systems, scalable inference infrastructure, large-scale retrieval, ranking or recommendation systems
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