Research Engineer Graduate (Seed-Infra-Inference-US) - ...

Location: SeattleTeam: TechnologyEmployment Type: RegularJob Code: A24665ResponsibilitiesThe Seed-Infra team combines ML system engineering and the art of machine learning to develop and maintain massively distributed ML training and Inference system/services around the world, providing high-performance, highly reliable, scalable systems for LLM/AIGC/AGI. In our team, you/'ll have the opportunity to build the large-scale heterogeneous system integrating with GPU/NPU/RDMA/Storage and keep it running stable and reliable, enrich your expertise in coding, performance analysis and distributed system, and be involved in the decision-making process. You/'ll also be part of a global team with members from the United States, China and Singapore working collaboratively towards unified project direction.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.Responsible for the machine learning system development of the company/'s large-scale models, researching new applications and solutions of related technologies in areas such as search, recommendation, advertising, content creation, conversation, and customer service, meeting the growing demand for intelligent interaction from users, and comprehensively improving users/' lifestyles and communication methods in the future world.Design and development of the architecture of large-scale machine learning systems, solving technical difficulties such as high concurrency, high reliability, and high scalability of the system.Covering various sub-directions of machine learning system, including resource scheduling, model training, model inference, data management, and workflow orchestration.Research and introduction of advanced technologies in machine learning systems, such as the latest hardware architecture, heterogeneous computing systems, and compiler-based optimization technologies.Work closely with the algorithm teams to optimize the algorithm and system jointly.QualificationsMinimum Qualifications: Final year or recent PhD graduate with a background in Computer Science, related technical field or equivalent industrial research experienceMust obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.Excellent coding ability, solid foundation in data structures and basic algorithms, proficient in C/C++ or Python, winners of ACM/ICPC, NOI/IOI and other competitions are preferred.Familiar with at least one mainstream machine learning framework (TensorFlow/PyTorch/Jax).Master the principles of distributed systems, and participated in the design, development, and maintenance of large-scale distributed systems.Strong sense of responsibility, good learning ability, communication ability, and self-motivation.Good communication and collaboration skills, able to explore new technologies with the team and promote technological progress.Preferred Qualifications: Prior experience in large-scale projects or papers with great influence in the field of large models.Familiar with NLP, CV-related algorithms, and technologies, and experienced in large model training and RL algorithms.Experience in one of the following fields: CUDA, RDMA, AI Infrastructure, HW/SW Co-Design, High-Performance Computing (cutlass, NCCL), ML Hardware Architecture (GPU, Accelerators, Networking), ML for System, and Distributed Storage.Demonstrated a related technical experience from previous internship, work experience, coding competitions, or publications.Curiosity towards new technologies and entrepreneurship.High levels of creativity and quick problem-solving capabilities.By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: #J-18808-Ljbffr

Created: 2025-10-04