QUALIFICATIONS

Minimum Qualifications-PhD in the fields of Electrical Engineering, Computer Science, Physics and related technical discipline.-Experience in research experience in the fields of ML algorithm or AI accelerator design-Experience with programming tools and languages such as C/C++, MATLAB, Python.Preferred Qualifications-Familiar with appropriate ML frameworks.-Good communication skills, open minded and enthusiastic about new technologies

About Team:PICO is a leading VR/AR platform with independent innovation and R&D capabilities, focusing on VR all-in-one technology. PICO is committed to offering immersive and interactive VR experiences to our customers, including providing tailor-made solutions for our enterprise clients in the fields of education and healthcare.We are looking for talented individuals to join us for this future position in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with Bytedance.Successful candidates must be able to commit between January 5,2026 to December 14, 2026Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).Candidates can apply for a maximum of TWO positions and will be considered for jobs in the order you applied for. The application limit is applicable to ByteDance and its affiliates’ jobs globally.
Responsibilities:-Innovate and develop a power-efficient, low-latency, highly-intelligent machine perception vision system in a VR/AR product.-Develop necessary training, data collection/generation pipeline along with state of art ML algorithm/framework for understanding of the scene and user behaviors.-Map the ML algorithm to the HW accelerator, identify performance bottleneck, optimize performance and efficiency, propose more efficient HW/SW co-design for AI accelerator.-Collaborate with the x-functions team and translate system requirements into a proper cloud-edge workload partition and edge ML accelerator HW/SW requirement.-Perform quantitative evaluations through the use of system modeling, FPGA prototyping for validating ideas and architecture options.