Silicon Valley Startup | Collaboration with Stanford Researchers
GenMD is a Stanford spin-off building AI and data infrastructure for healthcare. We are looking for an AI Research Scientist to work on large language models (LLMs), synthetic data generation, model evaluation, and scalable model training.
The company was built out of years inside Stanford Medicine, working closely with world-class researchers and clinicians. We have access to tens of millions of patients, longitudinal health records, and clinical notes. GenMD is just coming out of stealth, already revenue-generating, well-funded, and backed by premier investors with years of runway. We’re intentionally small, move fast, and are focused on building something fundamental. CEO is a former Associate Director of AI, Stanford Medicine, Stanford University.
What You'll Work On
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.
Explore Mixture-of-Experts (MoE), model ensembles, distillation, and other modern LLM techniques.
Translate research ideas into reproducible experiments and production systems.
Preferred Candidate
Strong hands-on expertise working with LLMs.
B.Sc. (minimum) in CS, AI, ML, NLP, or a related field; thesis work in LLM/NLP strongly preferred.
Publications in LLM/NLP are strongly preferred.
Strong Python skills with experience in PyTorch, Hugging Face, model training, fine-tuning, and evaluation.
What We Offer
Fully remote work.
Approximately 50% working-hour overlap with California office hours, typically 9 AM-1 PM Pacific Time.
Access to high-performance GPU servers, including A100 GPUs, for model training and fine-tuning.
Opportunity to collaborate with Stanford researchers.
Opportunities to develop benchmarks and publish research papers in conferences and journals.
Work directly on challenging, real-world LLM and healthcare AI problems.
Salary: BDT 70,000-120,000/month (based on experience and expertise).
Full-time employment (40 hours/week).
Start date: As soon as possible.
Summary
Train, fine-tune, optimize and evaluate large language models and build scalable synthetic data generation pipelines for healthcare AI. Requires B.Sc. minimum in CS, AI, ML or NLP and strong hands-on LLM expertise with Python, PyTorch and Hugging Face.
Responsibilities
Train, fine-tune, and optimize LLMs; 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; Explore MoE, ensembles, distillation
Qualifications
Strong hands-on expertise working with LLMs; Strong Python skills with experience in PyTorch, Hugging Face, model training, fine-tuning, and evaluation
Education requirements
B.Sc. (minimum) in CS, AI, ML, NLP, or a related field; thesis work in LLM/NLP strongly preferred
Experience requirements
Strong hands-on expertise working with LLMs; Publications in LLM/NLP are strongly preferred
