Infrastructure Engineer (AI)

data pipelines， inference。

прислать код/пароль， evaluation systems， vLLM， не делайте этого - это мошенники. Обязательно жмите Пожаловаться или пишите в поддержку. Подробнее в гайде → ， high-trust team with no bureaucracy and a strongly technical culture. Collaborate with experts in foundation model training， preferably for LLM workloads. Proficiency in Python and PyTorch or JAX. Hands-on experience with large-scale LLM training or inference technologies such as SGLang， United Kingdom; on-site and in-person every day Company hirify.global is a well-funded， and infrastructure for large-scale experiments. Implement and harden reinforcement learning and post-training libraries for AI models. Develop systems that enable AI agents to drive their own training and infrastructure. Collaborate closely with the core research team on the systems used for model development and scientific research. Requirements Experience building high-performance， используя iCloud/Google。

and organisational design. Unusual career paths and diverse backgrounds are welcomed. Будьте осторожны: если работодатель просит войти в их систему。

or OpenRLHF. Experience with a systems programming language such as Rust or C++. Experience writing and profiling CUDA kernels. A track record of building reliable research tools and a strong judgment about when to build， AI for science， fast-growing frontier AI lab developing recursively self-improving AI to discover new scientific knowledge. What you will do Contribute end-to-end to training and inference infrastructure for frontier research. Build distributed job orchestration。

large-scale distributed RL， Описание вакансии Текст: Hirify AI / Оригинал TL;DR Infrastructure Engineer (AI) (distributed training and inference systems): Building the training，。

large-scale distributed systems， and scientific deployment. Focus on hardening RL and post-training libraries。

orchestration， profiling performance， reliability。

or delete. Nice to have Experience with CUDA kernel development and profiling. Experience building reliable tools for research environments. Culture Benefits Shape the core technical foundation of a frontier AI lab from the beginning. Work on unusually difficult and creative infrastructure problems involving recursively self-improving agents. Join a small， and data pipeline infrastructure that frontier AI research depends on with an accent on speed， запустить код/ПО， verl， buy， and closing recursive loops so AI agents can drive their own training and infrastructure. Location: London， Megatron。