全职 Machine Learning Engineer 工作, 薪水, Medicoder 公司招聘中 - Ricebowl

Role Description

As a Machine Learning Engineer specializing in Large Language Models (LLMs) at Medicoder, you will operate at the intersection of state-of-the-art AI engineering and critical, real-world clinical deployment. You will own the end-to-end lifecycle of LLMs and deploy optimized, privacy-first solutions directly into production within local and international hospitals.

Key Responsibilities

• Collaborate closely with the product team to deeply understand user requirements, customer pain points, and product vision, translating them into robust, actionable technical specifications for model development.
• Rigorously evaluate and benchmark State-of-the-Art (SOTA) LLMs (both proprietary closed-source and open-source models) against complex, domain-specific medical and administrative tasks to ensure maximum product reliability.
• Fine-tune existing architectures and train domain-specific models from scratch using public, proprietary, and unstructured clinical datasets to directly improve product features.
• Translate AI capabilities into user value by deploying, monitoring, and maintaining high-performance LLM pipelines directly within local hospital environments and clinical workflows.
• Optimize models for real-world constraints, focusing on reducing latency, managing GPU memory footprint, and lowering inference costs so our customers experience a seamless, lightning-fast product.
• Curate, synthesize, and clean high-quality pre-training and fine-tuning datasets from complex, multi-modal medical records to continuously fuel product iterations.

Required Qualifications & Skills

• Master's degree or above in CS, AI, Mathematics, Statistics, Engineering or other related majors.
• Solid ML/DL theoretical foundation, in-depth understanding of LLM, NLP, Agent technologies; strong mathematical skills, excellent self-learning and problem-solving abilities; project implementation experience.
• Proficient in PyTorch/TensorFlow, Python/C/C++, with hands-on experience in large model training, fine-tuning and inference deployment, MLOps pipelines, Docker, and deploying models to cloud or on-premise GPU environments.
• A product-focused person who thrives in a fast-paced environment and is deeply motivated by solving systemic healthcare challenges rather than chasing abstract research goals.

Work Environment & Culture

• As an early member, you will directly shape the company's stack, architecture, and infrastructure while steering your own technical implementations in a fast-paced startup environment.
• You will work closely with leadership, engineering, and clients to build user-centric solutions that are rapidly deployed to assist clinical teams in real-time.
• Compensation packages will feature a highly competitive combination of salary, stock options, or a customized mix of both.