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Salary: BDT 70,000 - 120,000 / monthly
GenMD is a Stanford spin-off building AI and data infrastructure for healthcare. The company was founded by researchers from Stanford Medicine and is already revenue-generating and well-funded with significant runway. You'll work on large language models (LLMs), synthetic data generation, model evaluation, and scalable model training in a fully remote capacity with approximately 50% working-hour overlap with California office hours (typically 9 AM–1 PM Pacific Time).
In this role, you will train, fine-tune, and optimize LLMs using PyTorch and Hugging Face; build synthetic data generation pipelines at scale; develop LLM benchmarks, automated evaluation pipelines, and LLM-as-a-Judge systems; experiment with supervised, semi-supervised, and unsupervised learning approaches; explore advanced techniques such as Mixture-of-Experts (MoE), model ensembles, and distillation; and translate research ideas into reproducible experiments and production systems.
You'll have access to high-performance GPU servers including A100 GPUs for model training and fine-tuning, opportunities to collaborate with Stanford researchers, and the chance to develop benchmarks and publish research papers in conferences and journals. The role involves working directly on challenging, real-world LLM and healthcare AI problems.
REQUIREMENTS:
- Strong hands-on expertise working with LLMs
- B.Sc. (minimum) in Computer Science, AI, Machine Learning, NLP, or a related field; thesis work in LLM/NLP strongly preferred
- Publications in LLM/NLP strongly preferred
- Strong Python skills with experience in PyTorch, Hugging Face, model training, fine-tuning, and evaluation
