Machine Learning Scientist, ASR (All Levels)

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 SoHo neighborhood of New York, the Mission District in San Francisco, and Lawrenceville in Pittsburgh.

The Role

From transcribing medical parts of the conversation to delivering key takeaways, our trailblazing work in machine learning research makes the Abridge experience possible. We're currently looking for a research scientist with experience in speech recognition to help us improve the performance and robustness of our transcription modules.

The ideal candidate will bring technical mastery, fluency with foundation models, speech recognition, 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 ASR, in areas including accurate transcription of clinical conversations, support of a wide variety of languages and dialects, speaker diarization, domain-specific language modeling, robust handling of diverse accents and noise conditions, and development of novel evaluation and experimentation techniques.

• Actively contribute to the wider research community by sharing and publishing original research

• Research and implement algorithms and methods for long-form and real-time speech recognition

• Perform data preprocessing and define performance measures based on development and test sets

• Implement and improve tools, algorithms, and prototypes

• Be responsible for measuring and optimizing the quality of your algorithms

• 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

• 2+ years experience in building speech recognition systems

• Strong research background, as demonstrated through papers and a graduate degree (MS or PhD) in Electrical Engineering, Computer Sciences, Mathematics, or equivalent experience with a specialization in speech recognition or machine learning

• High-impact publications at peer-reviewed speech conferences (e.g. ICASSP, Interspeech, SLT) or NLP/ML conferences (NAACL, ACL, NeurIPS, ICML, ICLR)

• 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

• Deep knowledge of ASR technologies such as acoustic modeling, language modeling, neural networks, HMM, WFST, and feature extraction

• Hands-on experience in ASR tool kits such as Sphinx, Kaldi, HTK, or Julius

Ideally, You Have

• Research work or publications pertaining to applying deep learning methods to speech recognition

• Deep fluency in academic fields relevant to speech recognition

Base Salary: $200,000 USD - $300,000+ USD per year + Equity

The salary range provided is based on transparent pay guidelines and is an estimate for candidates residing in the San Francisco and New York City metro areas. The actual base salary will vary depending on the candidate's location, relevant experience, skills, qualifications, and other job-related factors. Additionally, this role may include the opportunity to participate in a company stock option plan as part of the total compensation package.

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 within 6 months of accepting an offer.

We value people who want to learn new things, and we know 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.

Why work at Abridge?

At Abridge, we’re driven by our mission to bring understanding and follow-through to every medical conversation. Our culture is founded on doing things the “inverse” way in a legacy system—focusing on patients, instead of the system; focusing on outcomes, instead of billing; and focusing on the end-user experience, instead of a hospital administrator's mandate.

Abridgers are engineers, scientists, designers, and health policy experts from a diverse set of backgrounds—an experiment in alchemy that helps us transform an industry dominated by EHRs and enterprise into a consumer-driven experience, one recording at a time. We believe in strong ideas, loosely held, and place a high premium on a growth mindset. We push each other to grow and expose each other to the latest in our respective fields. Whether it’s holding a PhD-level deep dive into understanding fairness and underlying bias in machine learning models, debating the merits of a Scandinavian design philosophy in our UI/UX, or writing responses for Medicare rules to influence U.S. health policy, we prioritize sharing our findings across the team and helping each other be successful.

How we take care of Abridgers:

• Generous Time Off: 13 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employees.

• Comprehensive Health Plans: Medical, Dental, and Vision plans for all full-time employees. Abridge covers 100% of the premium for you and 75% for dependents. If you choose a HSA-eligible plan, Abridge also makes monthly contributions to your HSA. 

• Paid Parental Leave: 16 weeks paid parental leave for all full-time employees.

• 401k and Matching: Contribution matching to help invest in your future.

• Pre-tax Benefits: Access to Flexible Spending Accounts (FSA) and Commuter Benefits.

• Learning and Development Budget: Yearly contributions for coaching, courses, workshops, conferences, and more.

• Sabbatical Leave: 30 days of paid Sabbatical Leave after 5 years of employment.

• Compensation and Equity: Competitive compensation and equity grants for full time employees.

• ... and much more!

Diversity & Inclusion

Abridge is an equal opportunity employer. Diversity and inclusion is at the core of what we do. We actively welcome applicants from all backgrounds (including but not limited to race, gender, educational background, and sexual orientation).

Staying safe - Protect yourself from recruitment fraud

We are aware of individuals and entities fraudulently representing themselves as Abridge recruiters and/or hiring managers. Abridge will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Abridge recruiting team will come from an @abridge.com email address. You can learn more about how to protect yourself from these types of fraud by referring to this article. Please exercise caution and cease communications if something feels suspicious about your interactions.