Responsible for designing and programming a small module or a large component and designing a feature, set of features, or whole feature area. She/he will work independently and contribute to the immediate team and to other teams across business. She/he will Lead design discussion in a limited manner.
GE HealthCare is a leading global medical technology and digital solutions innovator. Our purpose is to create a world where healthcare has no limits. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a difference, and every difference builds a healthier world.
Be responsible for the fine-tuning of large language models, including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and domain-specific model adaptation, to improve model performance and adaptability in vertical scenarios.
Conduct in-depth research and optimization on Prompt Engineering, design high-efficiency and high-robustness prompt templates, and explore advanced prompt strategies to enhance model output quality and task completion efficiency.
Optimize the Function Calling capability of large models, improve the accuracy, stability and generalization of model tool invocation, and realize the seamless connection between models and external tools and services.
Conduct research and implementation on advanced model reasoning technologies, including Chain of Thought (CoT), reflection mechanism, multi-step reasoning, and long context management, to solve complex reasoning tasks and extend the effective context window of models.
Build a comprehensive LLM evaluation system, conduct all-round testing and evaluation on models, focusing on model hallucination problems, output stability, tool call accuracy, reasoning ability and other core indicators, and put forward targeted optimization plans.
Track the cutting-edge research progress and technical trends in the field of large language models, introduce advanced algorithms and technologies into business scenarios, and promote the continuous iteration and upgrading of model technology.
Be responsible for the research and development of the core Agent runtime system, including the design and implementation of execution engine, state machine, memory module and tool call framework, to ensure the efficient, stable and scalable operation of the Agent system.
Develop and optimize multi-Agent collaboration mechanisms, realize core functions such as dialogue routing, conflict resolution, task decomposition and aggregation, and build a collaborative system for efficient interaction and task division among multiple Agents.
Optimize the scheduling logic and execution efficiency of the Agent engine, solve the problems of task delay, memory overflow and tool call failure in the Agent operation process, and improve the overall performance of the system.
Design the Agent system architecture with high availability and high scalability, support the access of various types of large models and external tools, and meet the needs of complex business scenarios.
Be responsible for the connection and secondary development of mainstream open-source frameworks in the field of Agent, including LangChain, AutoGPT, OpenCWA, etc., and independently develop customized Agent frameworks and components according to business needs.
Collaborate with product, engineering and other teams to translate algorithm research results into implementable technical solutions, and support the landing and application of large model products.
Participate in agile processes: planning, estimation, retros, and on-call (as needed)
MS or PhD degree or above in Computer Science, Artificial Intelligence, Mathematics, Statistics and other related majors, with solid theoretical foundation in natural language processing, machine learning and deep learning.
More than 3 years of relevant working experience in large language model algorithm research and development, familiar with the training, fine-tuning and inference process of mainstream open-source large models (such as LLaMA, Qwen, ChatGLM series, etc.).
Proficient in deep learning frameworks such as PyTorch, TensorFlow, familiar with model fine-tuning tools such as PEFT, LoRA, and have hands-on experience in large model parameter-efficient fine-tuning.
In-depth understanding of Prompt Engineering, Function Calling, Chain of Thought and other LLM related technologies, with practical project experience in model reasoning optimization and long context processing.
Experience in building LLM evaluation systems and conducting model hallucination, stability and tool call accuracy testing is preferred.
Proficient in Python programming, with good data structure and algorithm foundation, and strong code implementation and problem-solving abilities.
Have strong learning ability and innovative thinking, pay attention to industry cutting-edge dynamics, and have the ability to independently tackle key technical problems.
Good communication and collaboration skills, able to efficiently cooperate with cross-team members to promote project progress.
GE HealthCare is an Equal Opportunity Employer where inclusion matters. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
We expect all employees to live and breathe our behaviors: to act with humility and build trust; lead with transparency; deliver with focus, and drive ownership – always with unyielding integrity.
Our total rewards are designed to unlock your ambition by giving you the boost and flexibility you need to turn your ideas into world-changing realities. Our salary and benefits are everything you’d expect from an organization with global strength and scale, and you’ll be surrounded by career opportunities in a culture that fosters care, collaboration and support.
