R&D Engineer, Large Language Model

GE HealthCare

. Develop and optimize LLM, VLM, embedding, and audio multimodal models based on open-source foundation models.

Posted 10/15/2026full-timeShanghai • ChinaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing multimodal models, including LLMs and VLMs, with a strong focus on model fine-tuning, dataset management, and automated evaluation. Proficient in Python and Linux, with a solid understanding of Transformer architectures and experience in cross-functional collaboration.

Highest-signal resume keywords

Master's Degree In Computer ScienceExperience With LLMs And VLMsProficiency In Python And LinuxHands-On Experience With SFT And PEFTKnowledge Of TensorRT And ONNX Runtime

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model Fine-TuningDataset ManagementTransformer ArchitecturesGUI RecognitionEmbedding ModelsDistillationQuantizationAutomated-Control TasksRisk ManagementExperiment Traceability

Soft Skills

Effective Cross-Functional CollaborationSound Engineering Judgment

Tools & Technologies

PyTorchTensorRTONNX Runtime

Industry Keywords

Biomedical EngineeringArtificial IntelligencePattern RecognitionAutomated EvaluationModel Verification

Tech Stack

Tools & technologies

LinuxPythonPyTorch

About the role

Key responsibilities & impact

Develop and optimize LLM, VLM, embedding, and audio multimodal models based on open-source foundation models.
Build GUI-recognition and automated-control agents for medical post-processing software and medical devices.
Fine-tune models using PEFT methods including LoRA/QLoRA, SFT, and DPO.
Deliver GUI recognition, UI-element localization, instruction understanding, task planning, and tool-calling capabilities.
Build and manage multimodal datasets from screenshots, workflows, device commands, and audio interactions.
Establish automated evaluation and maintain versioning and experiment records for R&D traceability.
Apply distillation, pruning, and INT4/INT8 quantization to optimize models.
Optimize inference using KV cache, operator fusion, and memory reuse.
Convert and deploy models locally using TensorRT and ONNX Runtime.
Integrate VLM recognition, LLM planning, embedding retrieval, and ASR understanding into automated agent loops.
Support model verification, performance validation, technical documentation, risk analysis, boundary-case analysis, and evaluation of emerging technologies.
Collaborate with device software, Product, and Quality teams.

Requirements

What you’ll need

Master's degree or above in Computer Science, Artificial Intelligence, Pattern Recognition, Biomedical Engineering, or a related field.
Strong understanding of Transformer architectures, LLMs, VLMs, and PyTorch.
Hands-on end-to-end experience with SFT and parameter-efficient fine-tuning such as LoRA/QLoRA.
Experience with VLMs, GUI/screenshot visual understanding, and automated-control tasks.
Knowledge of embedding models, RAG, and ASR model tuning.
Knowledge of distillation, quantization, pruning, TensorRT, and ONNX Runtime.
Proficiency in Python and Linux; ability to independently build datasets, train and evaluate models, and complete model conversion and deployment.
Strong focus on experiment traceability, model boundaries, and risk management.
Sound engineering judgment to balance accuracy, latency, and hardware cost.
Effective cross-functional collaboration with device software, Product, and Quality teams.

Benefits

Comp & perks

Total rewards package
Salary and benefits
Career opportunities
Culture that fosters care, collaboration and support