Large Language Model R&D Engineer
职位描述
Job Description Summary
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, meeting product design, performance, and compliance-validation requirements. This role does not involve large-scale foundation-model pretraining.
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.
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Job Description
Multimodal Fine-Tuning &
- Agent Development
- Fine-tune LLM, VLM, embedding, and audio models using PEFT methods including LoRA/QLoRA, SFT, and DPO.
- Deliver GUI recognition, UI-element localization, instruction understanding, task planning, and tool-calling capabilities for device and software automation.
- Traceability
- Build multimodal datasets from software screenshots, workflows, device commands, and audio interactions; perform collection, cleaning, annotation, and augmentation.
- Establish automated evaluation for recognition accuracy, instruction-following success, hallucination rate, and robustness; maintain versioning and experiment records for R&D traceability.
- Deployment
- Apply distillation, pruning, and INT4/INT8 quantization (PTQ/QAT) to reduce model size, memory, and compute cost while preserving accuracy.
- Optimize inference through KV cache, operator fusion, and memory reuse; convert and deploy models locally using TensorRT and ONNX Runtime to meet latency and throughput targets.
- Research
- Integrate VLM interface recognition, LLM planning, embedding retrieval, and ASR understanding into an automated agent loop with engineering teams.
- Support model verification, performance validation, technical documentation, risk analysis, boundary-case analysis, and evaluation of emerging VLM, Agent, GUI, and lightweight-model technologies.
Master's degree or above in Computer Science, Artificial Intelligence, Pattern Recognition, Biomedical Engineering, or a related field.
Fundamental Knowledge
- 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 (for example, LLaVA), 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.
- Experience in medical devices, medical-imaging post-processing software, medical-device AI, or medical-device software quality systems.
- Practical experience with agent development, tool calling, and orchestration frameworks such as LangChain or LangGraph.
- Experience optimizing models for embedded or compute-constrained local devices; familiarity with C , inference SDKs, or vector databases.
- 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
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.
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