Senior Machine Learning Engineer, LLMs and Clinical NLP

• Build and improve LLM-based clinical NLP systems, including summarization, structured extraction, and controlled generation
• Train, finetune, and post-train LLMs using approaches such as supervised finetuning and preference or feedback-driven optimization where appropriate
• Design evaluation strategies for clinical text generation, including offline benchmarks, human review workflows, slice-based analysis, and quality gates aligned with clinical risk
• Develop and operate LLM inference services using vLLM, with focus on reliability, scalability, and practical performance
• Optimize inference for latency, throughput, and cost, for example batching, caching, quantization, and decoding strategy improvements
• Build and maintain APIs and services using FastAPI, and deploy and run them on Kubernetes
• Take technical ownership of core NLP components, shaping best practices for model development, evaluation, and production reliability across the team, and supporting the growth of engineers working on text generation systems
• Partner with researchers, engineers, and product teams to ship improvements end-to-end, including observability and monitoring to support continuous iteration

• Strong programming skills in Python and the ability to contribute to production-grade codebases
• Hands-on experience with LLMs for NLP or text generation, including at least some of the following: Training, fine-tuning, or post-training transformer-based models
• Building or operating LLM inference services in production, including performance work
• Designing robust evaluations for generative systems, including metrics, error analysis, and human evaluation methods
• Experience turning research outcomes into practical systems that can be validated and shipped
• Familiarity with building ML systems beyond notebooks, such as data pipelines, CI/CD practices, monitoring, and deployment workflows
• Clear communication and collaboration skills across research, engineering, and product
• A Master’s degree in computer science, engineering, mathematics, statistics, physics, or a related field, or equivalent professional experience

experience with healthcare data, clinical NLP, privacy and safety considerations, or working with domain experts in evaluation

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