Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

Machine Learning / NLP Research Scientist - (REMOTE)

(multiple opportiunities at various levels)

Our client is at the forefront of applying machine learning research to transform the way medical conversations are understood and utilized. We are in search of research scientists who are proficient in machine learning and natural language processing to join our innovative team. The ideal candidate will possess a deep understanding of foundation models, a keen interest in healthcare applications, and the ability to think critically and solve complex problems. Our work is deeply rooted in research, with every member of our team contributing to the development of real-world applications that significantly benefit healthcare professionals.

**What You'll Do**

- Push the boundaries of medical NLP research, focusing on conversation summarization, evidence extraction, outcome prediction, and developing new evaluation techniques and experimental approaches.

- Engage with the broader research community by disseminating original research findings and insights.

- Tackle significant challenges, set benchmarks, innovate state-of-the-art methodologies, and integrate them into practical applications.

- Utilize feedback from healthcare professionals to drive continuous improvement and innovation in our solutions.

- Approach ambiguous challenges and uncertain results with a results-driven mindset.

**What You'll Bring**

- A solid research foundation, evidenced by publications and an advanced degree (MS or PhD) in Electrical Engineering, Computer Sciences, Mathematics, or a related field.

- Contributions to leading AI conferences (e.g., *CL, NeurIPS, ICML, ICLR) through high-impact publications.

- Demonstrated real-world impact via open source contributions and the deployment of technologies.

- Proficient programming skills and experience in developing, prototyping, and implementing machine learning solutions in a production environment.

- Familiarity with deep learning frameworks (e.g., PyTorch, Jax, TensorFlow), experience with multi-GPU training, and a strong capability in statistical analysis of both observational and experimental data.