# Machine Learning Scientist (All Levels)

**Company:** [Abridge](https://hotfix.jobs/companies/abridge)
**Location:** San Francisco, CA, New York, NY, Pittsburgh, PA
**Role:** AI Research
**Salary:** $205k – $300k/yr
**Skills:** PyTorch, JAX, TensorFlow, Natural Language Processing, Machine Learning, Deep Learning, Multi-Gpu Training, Statistical Analysis, Foundation Models
**Posted:** 2025-07-11

> Conducts machine learning research in medical NLP for conversation summarization, evidence extraction, and outcome prediction. Publishes at top AI conferences, deploys models to production, and requires MS/PhD plus strong PyTorch/TensorFlow experience.

## Job Description

## What You'll Do
- Advance the state of the art in medical NLP, in areas including conversation summarization, evidence extraction, outcome prediction, evaluation techniques, and experimentation.
- Actively contribute to the wider research community by sharing and publishing original research
- Help to define important problems, identify appropriate baselines, develop state-of-the-art methods, and ship them into production.
- Dial deeply into real-time feedback from clinicians to guide further refinements and innovations
- Be results-oriented in the face of ambiguous problems and uncertain outcomes

## What You'll Bring
- Strong research background, as demonstrated through papers and a graduate degree (MS or PhD) in Electrical Engineering, Computer Sciences, Mathematics, or equivalent experience.
- High-impact publications at peer-reviewed AI conferences (e.g. ACL, COLM, EACL, NAACL, NeurIPS, ICML, ICLR, Interspeech, ICASSP, SLT, MLHC, ML4H).
- Significant real-world impact, as demonstrated through open source contributions and deployed technology.
- Strong programming skills with proven experience crafting, prototyping, and delivering machine learning solutions into production.
- Experience with deep learning libraries (e.g. **PyTorch**, **Jax**, **Tensorflow**) and platforms, multi-GPU training, and statistical analyses of observational and experimental data.

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