Large Model Optimization Engineer Graduate(PICO Perception) - 2026 Start (PHD)

As a technology brand with independent innovation and R&D capabilities, PICO is committed to becoming a leading XR platform, helping developers and creators achieve success, and jointly creating a better life experience for global consumers. It has pioneered the expansion of virtual reality into multiple fields, bringing new experiences to consumer-grade scenarios such as sports, video, and entertainment, and is widely used in commercial scenarios such as education, medical care, and corporate training, providing diversified VR solutions for global enterprise users.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at ByteDance

Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.

Responsibilities

• Participate in the research and development of inference optimization and acceleration for models such as LLM/VLM/SD, as well as inference engines and frameworks;
• Build an industry-leading on-device large model inference engine through high-performance optimization technologies such as efficient operator development, low-precision computing, streaming inference, and speculative sampling;
• Collaborate deeply with various algorithm teams to analyze business performance bottlenecks and conduct performance analysis and optimization for large models;
• Participate in the performance evaluation of models on different chips.

Qualifications

• Final year Ph.D or recent Ph.D graduates in Computer Science, engineering or quantitative field
• Proficiency in C/C++ and Python under the Linux environment;
• Ability to skillfully use at least one mainstream machine learning framework, with preference given to those familiar with various model/data parallel training frameworks;
• Knowledge of mainstream models such as LLM/VLM/SD with experience in model inference optimization preferred;
• Experience in performance modeling, performance analysis and optimization, or knowledge of CPU and GPU architectures is preferred; experience in GPU programming (CUDA or OpenCL) and familiarity with TensorRT/Triton/Cutlass is preferred;
• Those with top conference papers in the direction of AutoML or AIGC are preferred; those who have published papers in top computer vision conferences or journals are preferred; those who have achieved excellent results in well-known computer vision competitions are preferred; those with experience in high-quality Github projects are preferred.