Senior AI Scientist
$180,000–$180,000 year
RemoteUnited States
Job Summary
Senior AI Scientist at the Atria Health Institute responsible for owning the medical-modeling roadmap and developing domain-specific clinical AI models as well as foundation models for preventive medicine. Work encompasses multi-modal longitudinal data (e.g., whole-genome sequencing, advanced imaging, comprehensive labs, wearable signals, and family-linked records), turning open-source components into clinically impactful models, designing novel architectures when needed, and guiding fine-tuning, post-training, and evaluation. Collaborate with clinicians to define good clinical outcomes and align research with real-world healthcare delivery, applying SFT/LoRA/QLoRA, DPO/RLAIF, RLHF, and distillation within robust experimentation pipelines, while staying current with the literature and ensuring PHI-safe data handling.
Required Qualifications
- What you'll bring
- 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.
Desired Qualifications
- Experience building multimodal medical models, combining clinical text with imaging (radiology, pathology, ophthalmology), structured labs, or physiological signals.
- Familiarity with clinical/biomedical foundation models (e.g., Med-PaLM, MedGemma, BioMedLM, BiomedCLIP, RadFM) and the medical model literature.
- Peer-reviewed publications in ML, NLP, computer vision, or clinical informatics venues. • Experience with reinforcement learning, reasoning model training, or other frontier post-training techniques. • Experience with model interpretability and uncertainty quantification for clinical settings. …
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