Senior ML Engineer (all levels)

Job description

Role Overview

At the HKRI AI Infrastructure Team, we are building a world-class research group dedicated to AI for Science, with a long-term vision to push the boundaries of artificial intelligence and drive meaningful scientific and societal impact. Our work spans cutting-edge domains including large language model (LLM) inference acceleration, machine learning-based force field simulation, and generative AI (GenAI) acceleration. We operate advanced laboratories in Ningde and Hong Kong, fostering deep collaboration between systems, algorithms, and domain science.

We’re seeking passionate Machine Learning Engineers who thrive at the intersection of systems and AI to help us build the next generation of AI infrastructure.

Responsibilities

Research, evaluate, and integrate frontier technologies in ML systems—such as novel GPU architectures, compiler optimizations, custom operator design, and etc.

Work across the full ML system stack

resource scheduling, distributed training & inference, data management, and workflow orchestration. Design, develop, and optimize a high-performance, developer-centric machine learning platform that sets new industry standards. Collaborate closely with algorithm teams (e.g., mechanistic modeling, organic/inorganic materials discovery) to co-optimize models and systems for scientific applications.

Qualifications

Bachelor’s, Master’s, or PhD in Computer Science, Software Engineering, Artificial Intelligence , or a related field. Strong foundation with 3+ years of in software engineering and machine learning algorithms , with hands-on coding experience. A genuine passion for technology—you enjoy writing code (C++/Python/React/Node), debugging complex issues, and digging deep into system internals. Proven ability to tackle hard problems with patience, curiosity, and analytical rigor.

Highly self-motivated, responsible, and eager to learn; excellent communicator and team player.

Nice-to-Have (Preferred Qualifications)

Familiarity with LLMs, NLP, CV, and etc. , especially in the context of large-scale models, ML force fields, or generative AI . Experience in one or more of the following areas:ML hardware (GPUs, accelerators, interconnects)；CUDA, RDMA, or high-performance networking；AI infrastructure, distributed systems, or HPC；Hardware/software co-design；ML for Systems or distributed storage.