GE医疗
Staff Software Engineer
Staff Software Engineer
发布于 大约 16 小时前普通员工/个人贡献者
北京市
高级经验
全职员工
仅现场办公
硕士
软件工程
Chain Of Thought
Fine-Tuning
Llm
Prompt Engineering
Pytorch
Rlhf
Sft
AI 估算 · 40k–60k
LLM方向人才稀缺，GE医疗作为跨国巨头提供具有竞争力的薪资，结合北京市场行情和Staff级别，预估月薪40k-60k，15薪。
职位详情
关于这个职位
作为GE医疗的Staff软件工程师，您将专注于大语言模型（LLM）的研发与优化，包括模型微调、推理增强、Agent系统构建等核心任务
您将参与前沿AI技术在医疗场景的落地，与跨团队协作推动产品创新
该职位要求扎实的机器学习基础和LLM实战经验，适合有志于在AI+医疗领域深耕的技术专家
最低要求
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.
工作职责
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)
优先资格
CUDA/CuFFT/CuBLAS, OpenMP, SIMD vectorization
Experience with git, jenkens, devops, etc.
Publications/competitions or open-source contributions
AI 洞察
优缺点分析
优点
- GE医疗作为全球医疗科技巨头，平台大、资源丰富，项目有实际医疗落地场景，社会价值高
- 聚焦LLM和Agent前沿技术，技术栈新，个人成长空间大，能积累稀缺的AI工程经验
- 薪资待遇有竞争力，作为Staff级别员工，能够获得较好的薪酬和福利
- 需要与多团队协作，沟通成本高，且需应对复杂的业务需求和技术难题
- 北京现场办公，可能面临通勤和加班问题，工作生活平衡需自我调节
- 适合对LLM和Agent技术有热情、追求技术深度与前沿、能接受一定工作强度的算法工程师
缺点 / 挑战
- 医疗行业对AI准确性要求极高，模型幻觉和稳定性挑战大，工作压力可能较大
角色解读
- 在GE医疗内部，可向高级算法专家或技术负责人发展，主导更大规模的AI项目
- LLM和Agent方向是行业热点，积累经验后可跳槽至其他顶级科技公司或创业
- 有机会参与医疗AI前沿研究，发表论文或贡献开源，提升行业影响力
- 负责大语言模型的微调与优化，包括SFT、RLHF等，提升模型在医疗等垂直场景的表现
- 研究并优化Prompt Engineering与Function Calling，提升模型任务完成效率和工具调用能力
- 开发Agent运行时系统，设计多Agent协作机制，确保系统高可用与可扩展
- 构建LLM评估体系，测试模型幻觉、稳定性等核心指标，推动模型迭代
- 扎实的NLP、机器学习与深度学习理论基础，熟悉主流开源大模型（如LLaMA、Qwen）
- 精通PyTorch或TensorFlow，掌握PEFT、LoRA等微调工具，有实际模型微调经验
- 深入理解Prompt Engineering、Function Calling、Chain of Thought等LLM技术
- 有Agent系统开发经验，熟悉LangChain等框架，具备Python编程和算法功底
申请策略
- 在简历和面试中强调结果导向，用具体指标（如模型准确率提升、延迟降低）证明贡献
- 了解GE医疗的AI产品方向（如影像诊断、基因组学），在面试中展现对医疗场景的兴趣
- 突出大规模语言模型微调项目经验，特别是SFT、RLHF等具体案例和成果
- 展示Prompt Engineering、Function Calling或Agent系统开发的实际项目，附上效果数据
- 强调开源贡献、技术博客或相关论文，体现技术影响力
- 列举使用PyTorch、LangChain、PEFT等工具的经验，以及解决模型幻觉、推理优化的经历
- 补充Agent框架（如LangChain、AutoGPT）的实践经验，尤其是多Agent协作和运行时系统
- 提升系统设计能力，学习高可用架构和性能优化，以应对Agent系统的工程挑战
面试指南
- 针对项目类问题：遵循STAR原则（情境、任务、行动、结果），突出技术难点和你的贡献
- 对于设计类问题：先明确目标，然后分析关键设计决策（如Agent通信机制、状态管理），最后评估方案优劣
- 对于原理类问题：先定义概念，再对比不同方法（如CoT vs. Few-shot），最后结合实际案例说明应用
- 请介绍一个你使用RLHF微调LLM的项目，遇到了哪些挑战？如何解决？
- 如何设计一个多Agent协作系统来解决任务分解与冲突？
- Prompt Engineering中，你如何设计prompt以减少模型幻觉？
- 请解释Chain of Thought的原理，并举例说明如何在复杂推理任务中应用
- 如何评估LLM的Function Calling准确性？你会设计哪些测试指标？
职位点评
77
综合评分
GE医疗Staff算法岗，LLM前沿技术栈，薪资优厚，但工作地点固定且可能加班。
从薪资福利、成长空间、工作节奏和岗位方向综合评估，方便横向比较。
更适合这类人
适合追求技术前沿和职业成长、且能接受现场办公和一定工作强度的求职者。
表现最好
成长发展
相对薄弱
工作生活
薪资福利85
成长发展95
工作生活40
使命价值75
薪资福利
85较高
该职位薪资具有竞争力，GE医疗作为大厂提供稳定薪酬和福利，但JD未明确列出具体福利，综合评估满足度较高。
薪资信号未披露（AI估算：40K-60K/月）
成长发展
95较高
职位聚焦LLM和Agent最前沿技术，涉及微调、推理、多智能体等核心领域，技能成长空间极大，且公司平台助力职业发展。
技术前沿前沿/新兴技术
技术栈LLM、SFT、RLHF、Prompt Engineering、Function Calling、Chain of Thought、Agent、LangChain、PyTorch
业务类型profit_center
工作生活
40较低
北京现场办公，未提及远程或弹性工作，且需参与on-call，工作生活平衡可能受影响。
工作模式仅现场办公
办公地点市区核心地段
加班情况未提及（无法判断）
使命价值
75中等
GE医疗致力于改善医疗健康，有正向社会影响力，但JD未强烈突出使命感，整体意义感中等偏上。
行业发展稳定成熟行业
社会影响正向社会影响力较高
使命信号create a world where healthcare has no limits
创新程度积极采用新技术
GE医疗 的其他在招职位
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