• A bias toward shipping models, not papers about models. You would rather have a working v1 in front of clinicians next month than a beautiful methodology that ships next year.
• A scrappy streak. You can pick up an unfamiliar fine-tuning technique, training framework, or clinical concept on a Wednesday and have a credible experiment running by Friday.
• A serious drive to keep getting better. You read other people’s code, papers, training logs, and post-mortems. You treat being wrong as cheap information.
• Graduate degree (PhD preferred, Master’s with strong research record) in computer science, machine learning, computational biology, biomedical informatics, or a closely related field — or a strong open-source track record in modern training and fine-tuning.
• Hands-on experience training and fine-tuning modern deep learning models, with a track record of shipped or published models you personally trained: 4+ years.
• Deep, current fluency with modern fine-tuning and post-training methods: SFT, PEFT (LoRA, QLoRA, adapters), preference tuning (DPO and successors), distillation, and continued pre-training.
• Strong working knowledge of the open-source model ecosystem: which models are state of the art, which are overrated, and what’s worth fine-tuning for a given problem.
• Strong Python and PyTorch, with hands-on experience with the Hugging Face ecosystem (transformers, datasets, PEFT, TRL, accelerate) or equivalent training stacks.
• Practical experience with training infrastructure: distributed training, mixed precision, efficient data loading, experiment tracking (W&B;, MLflow, or similar).
• Discipline around evaluation and ablation: you treat benchmarking, calibration, and “what would this have looked like without that change?” as part of the modeling work.
• Genuine interest in healthcare and the responsibility that comes with building models that affect patient care.