Machine Learning Scientist (All Levels)

About Abridge

Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients.

Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems.

We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh.

The Role

From transcribing medical conversations to delivering key takeaways, our trailblazing work in machine learning research makes the Abridge experience possible. We're currently looking to hire research scientists with experience in machine learning and natural language processing. The ideal candidate will bring technical mastery, fluency with foundation models, genuine interest in the medical domain, and strong critical thinking skills to the role. At Abridge, all of our ML work has a strong research component, and all of our research scientists contribute directly to real products that impact the lives of doctors.

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.

We value people who are eager to learn new things and recognize that great team members might not perfectly match a job description. If you’re interested in the role but aren’t sure whether or not you’re a good fit, we’d still like to hear from you.

Must be willing to work from our SF office at least 3x per week

This position requires a commitment to a hybrid work model, with the expectation of coming into the office a minimum of (3) three times per week. Relocation assistance is available for candidates willing to move to San Francisco.

Why Work at Abridge?

At Abridge, we’re transforming healthcare delivery experiences with generative AI, enabling clinicians and patients to connect in deeper, more meaningful ways. Our mission is clear: to power deeper understanding in healthcare. We’re driving real, lasting change, with millions of medical conversations processed each month.

Joining Abridge means stepping into a fast-paced, high-growth startup where your contributions truly make a difference. Our culture requires extreme ownership—every employee has the ability to (and is expected to) make an impact on our customers and our business.