About the Job
At Prenuvo, we are on a mission to flip the paradigm from reactive “sick-care” to proactive health care. Our award-winning whole body scan is fast (under 1 hour), safe (MRI has no ionizing radiation), and non-invasive (no contrast). Our unique integrated stack of optimized hardware, software, and increasingly AI, co...
We are looking for a Senior AI Applied Scientist II to independently own a model domain end-to-end within our AI Research org, from problem framing through validated, production-deployed models.
As Prenuvo’s AI org moves from task-specific models toward foundational work (meaning pre-trained backbones, multi-task heads, and the addition of clinical text and structured data), we need scientists who design experiments rather than only run them, diagnose failure modes on their own, and shape how the team appro...
Key Responsibilities
LLM Fine-Tuning & Domain Adaptation: Lead the end-to-end fine-tuning of large language models (SFT, PEFT/LoRA, DPO/RLHF) on clinical text, radiology reports, and longitudinal patient histories to build specialized, domain-adapted clinical models.
Agentic AI System Architecture: Design, develop, and operationalize autonomous and semi-autonomous AI agent systems utilizing multi-step reasoning, tool use, workflow orchestration, planning, and memory architectures to automate and accelerate complex research and clinical workflows.
Comprehensive NLP & LLM Evaluation: Establish robust, automated, and human-in-the-loop NLP evaluation frameworks to benchmark clinical accuracy, detect hallucinations, assess reasoning capabilities, and ensure safety, reliability, and regulatory compliance of deployed language models.
Multimodal Integration: Collaborate on multimodal systems connecting imaging foundation models with LLMs, structured lab data, and clinical text to create holistic patient insight platforms.
Experiment Design & Validation: Own experiment design and validation study scoping for your domain, diagnosing failure modes independently and establishing rigorous scientific benchmarks.
Data Quality & Clinical Framing: Drive data quality standards, clinical annotation criteria, and knowledge grounding (e.g., RAG, ontology alignment) for language and agentic pipelines in close collaboration with medical experts.
Technical Leadership & Collaboration: Partner across clinical, annotation, and ML Engineering teams, raising the code-quality bar and contributing reusable architectures and frameworks adopted org-wide.
Production Delivery: Deliver production-ready LLM and agentic models with documented validation, clinical sign-off, and continuous monitoring benchmarks, owning the technical narrative for your domain.
Required Skills & Abilities
Strong MSc or PhD from a top-tier institution in CS, computational linguistics, AI/ML, statistics, biomedical engineering, or a related field.
A minimum of 4 academic or industry years of ML/NLP experience, with a strong publication or applied-research record including first-author works in top ML/NLP venues (e.g., NeurIPS, ACL, EMNLP, NAACL, ICML, ICLR).
Expert-level PyTorch and proficiency with modern LLM fine-tuning libraries (e.g., Hugging Face Transformers, Unsloth, vLLM, DeepSpeed, Axolotl).
Hands-on expertise in LLM Fine-Tuning & Alignment: Supervised Fine-Tuning (SFT), Parameter-Efficient Fine-Tuning (LoRA, QLoRA), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) applied to open-source foundation models (e.g., Llama, Qwen, Mistral).
Demonstrated depth in Agentic AI Architectures: Designing and deploying LLM agents using frameworks like LangGraph, AutoGen, or CrewAI, implementing tool use/calling, ReAct prompting, multi-agent coordination, external memory, and multi-step execution.
Proven track record in NLP Evaluation & Grounding: Designing custom evaluation suites, benchmark creation, automated evaluation metrics (e.g., ROUGE, BERTScore, LLM-as-a-Judge), hallucination mitigation, semantic search, and Retrieval-Augmented Generation (RAG).
Demonstrated ability to own an LLM/Agent model domain end-to-end (architecting, training, evaluating, deploying to production performance standards).
Strong collaboration skills across ML Engineering, Product, and Clinical Ops, balancing scientific rigor with rapid delivery.
Experience fine-tuning LLMs on medical or clinical text corpora (e.g., EHRs, DICOM structured reports, PubMed).
Experience integrating vision-language models (VLMs) with medical imaging pipelines.
Familiarity with FDA guidelines for AI/ML-based Software as a Medical Device (SaMD) or clinical decision support tools.
Prior experience in health tech, biotech, or clinical AI environments.
