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Specialized senior ML/LLM role with niche healthcare data needs reduces candidate pool despite known company.
Highly domain-specific healthcare and multi-GPU training experience makes skills less transferable across industries.
Requires deep LLM training, multi-node production experience, and healthcare domain expertise, so filters are strict.
Own and build complex AI model systems behind healthcare products, including fine-tuning, model composition, production deployment, and maintaining product-level accuracy.
Lead post-training and domain adaptation of language models from 1B to 100B+ parameters, including handling multi-node GPU training and evaluating model performance in real clinical contexts.
Develop and maintain pipelines turning raw, multi-format healthcare data into training-ready datasets and build multi-step agentic AI that reliably performs operational healthcare tasks under latency and cost constraints.
MS or PhD in Computer Science, Machine Learning, or related quantitative field; exceptional BS candidates with strong research/open-source work considered.
Strong Python and PyTorch skills with experience fine-tuning open-weight language models and familiarity with training/serving stacks (e.g., HuggingFace, FSDP, DeepSpeed).
Hands-on multi-node training experience on models with tens of billions of parameters or larger using tens of GPUs, including distributed training strategies.
Work Experience Required: Senior AI Engineer level requires prior production experience with post-trained models, building model layers behind real product features, and owning data pipelines for training runs.
Experienced in product-focused AI modeling with ability to scope ambiguous problems into clear plans differentiating research from engineering tasks.
Demonstrated expertise in handling large-scale, multi-GPU training infrastructure and optimizing model latency/cost in real-world healthcare environments.
Background in regulated or high-stakes domains with a focus on rigorous evaluation methodology, data-centric research, and production-grade AI system reliability.