AI Scientist – Natural Language, Health Records

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September 25

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Artisight

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Healthcare Insurance • Artificial Intelligence • SaaS

Artisight is a healthcare technology company that offers a Smart Hospital Virtual Nursing Platform designed to optimize clinical workflows and improve patient care. Their platform uses computer vision, multi-sensor networks, and artificial intelligence to automate tasks, enhance communication, and provide real-time actionable data. Artisight's solutions aim to reduce clinician workload, improve patient safety, and increase operational efficiency. The company is focused on addressing the current challenges in healthcare, such as health equity and patient access, by creating a more human-centered and future-ready healthcare environment.

51 - 200 employees

⚕️ Healthcare Insurance

🤖 Artificial Intelligence

☁️ SaaS

💰 $42M Series B on 2024-01

📋 Description

• Train, fine-tune, and optimize large-scale NLP models for clinical text and health record data. • Develop methods for extracting insights from structured and unstructured health records, including notes, lab results, imaging reports, and longitudinal patient histories. • Contribute to the architecture of Artisight’s AI platform, ensuring seamless integration of health record intelligence with other AI modalities (vision, audio, IoT sensors). • Collaborate closely with AI scientists, engineers, clinicians, and product teams to translate research ideas into production-ready solutions. • Stay up to date with advances in clinical NLP, generative models, and multimodal AI, and bring these insights into Artisight’s applied research pipeline. • Share research outcomes through internal discussions, technical reports, and potentially external publications.

🎯 Requirements

• M.S. or Ph.D. in computer science, electrical engineering, biomedical informatics, applied AI, machine learning, or a related discipline. • Demonstrated expertise in clinical NLP or health records AI research, evidenced by open-source contributions or peer-reviewed publications (e.g., NAACL, ACL, NeurIPS, ICML, AMIA, JAMIA). • Hands-on experience with one or more of: Clinical named entity recognition (NER) and relation extraction; De-identification and privacy-preserving text processing; Clinical summarization, cohort selection, and patient timeline modeling; Predictive modeling for outcomes, risk stratification, or decision support; Integration of structured and unstructured EHR data. • Proficiency in deep learning techniques such as transformers, diffusion models, self-supervised learning, and sequence-to-sequence architectures. • Strong coding and experimentation skills with frameworks such as PyTorch or TensorFlow. • Experience with large-scale training and deployment tools (NVIDIA Triton, ONNX, or similar). • A collaborative mindset and ability to communicate research findings clearly to both technical and non-technical audiences. • Nice to haves: Experience with multimodal learning (EHR + imaging + audio + sensor data); Familiarity with federated learning and privacy-preserving AI approaches; Experience deploying AI in real-world healthcare environments; Contributions to open-source health NLP projects (e.g., MedSpaCy, Hugging Face Transformers for clinical text, cTAKES, BioBERT, ClinicalBERT).