обновлено 4 дня назад
Machine Learning Engineer (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Machine Learning Engineer (AI): Building and improving search, retrieval, LLM post-training, multi-agent systems, and agent harnesses with an accent on reinforcement learning, evaluation, and production-scale experimentation. Focus on training agents to reason and use tools, coordinating multi-agent workflows, and optimizing quality, latency, and cost across models, infrastructure, and search systems.
Location: Hybrid in Belgrade, Berlin, or London
Company
Perplexity develops AI-powered search and retrieval systems, including language models, agent harnesses, and search infrastructure.
What you will do
- Improve search and agent quality through advances in models, training data, tools, and system design.
- Develop LLM post-training methods, including reinforcement learning, for reasoning, search, tool use, and task completion.
- Train and evaluate multi-agent systems that divide work, share information, and coordinate across complex tasks.
- Design agent harnesses with context management, execution environments, tools, and orchestration for reliable multi-step work.
- Improve retrieval and ranking models and the search interfaces used by agents.
- Own experiments from hypothesis and scalable training through deployment and measurable improvements in quality, latency, and cost.
Requirements
- Experience building and shipping machine learning systems, with depth in LLM post-training, reinforcement learning, search and retrieval, or agent systems.
- Strong software engineering skills across model training, experimentation infrastructure, and production systems.
- Experience designing rigorous evaluations, diagnosing failures, and converting experimental results into practical improvements.
- Comfort solving open-ended problems that require research judgment and hands-on engineering.
- Ability to collaborate across AI, Search, Infrastructure, Data, and Product teams.
Nice to have
- Experience training models to use tools or complete multi-step tasks.
- Experience with multi-agent training, coordination, or evaluation.
- Experience building agent harnesses, distributed training systems, or scalable inference infrastructure.
- Experience with large-scale retrieval, ranking, or recommendation systems.
Culture & Benefits
- Work across the full stack, from models and agent harnesses to search infrastructure.
- Carry ideas from experiments through training, evaluation, deployment, and production.
- Value depth in a relevant area and the ability to learn across the stack.
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