Anthropic has opened vacancies for engineers to develop their own AI chips
8/5/2026, 02:35 PM • Евгения Слив

The company Anthropic has officially published vacancies for specialists in the development of modern AI chips. A representative of the organization confirmed to Business Insider the creation of the first internal hardware team. Engineers will design specialized chips together with advanced neural network models. This approach will significantly improve the overall speed and efficiency of computing. Candidates must have proven experience in successfully bringing semiconductors to mass production. The annual salary for one of the open positions reaches 485 thousand dollars. Specialists will have to make key technical decisions without the support of large engineering groups.
The development of proprietary processors is becoming a noticeable industry trend among leading technology corporations. Companies are striving to reduce dependence on the monopoly position of Nvidia, the manufacturer of graphics accelerators. Representatives of Anthropic separately emphasized the preservation of the strategy of using various hardware solutions. The organization will continue to actively use computing infrastructure from AWS, Google, Nvidia and AMD. The integration of third-party equipment will help maintain the stable operation of large-scale neural networks. Parallel creation of proprietary chips will provide additional optimization of specific learning algorithms.
Other major market players are also actively developing their own areas of microelectronics. The Reuters edition previously reported on the development of specialized chips by the Chinese company DeepSeek. OpenAI Corporation has successfully introduced its first core processor called Jalapeño. ByteDance is actively negotiating with Samsung Electronics on the production of microchips. Apple, together with Broadcom, is developing specialized computing units for AI tasks. The formation of independent supply chains requires significant financial and time costs. Industry participants are closely monitoring the evolution of hardware for training neural networks.
