Machine Learning Engineer (LLM)

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. You will work with a diverse data ecosystem, including public benchmarks, proprietary datasets, and highly sensitive clinical data. Because our systems interface directly with critical hospital infrastructure, you will balance maximizing raw model capability with the strict requirements of data privacy, low-latency performance, and product reliability. 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 Bachelor's or Master's degree or above in CS, AI, Mathematics, Statistics, Engineering or 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.