- Location
- Boston
- Level
- Mid level
- Posted
- 18 months ago
About this role
Layer Health was founded in 2023 by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. Our LLM-powered platform is solving chart review once and for all, across use cases. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology, to oncology. Our long term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine.
We’re seeking outstanding hires to join our team as early members. This is an opportunity to contribute to a high-impact, collaborative, mission-driven team, and help define the next stage of growth for Layer Health. Together, we will create the AI layer that will redefine healthcare for the better.
Here’s a collection of articles about our product, mission, recent funding round, etc.
Job Description
We’re hiring an exceptional ML scientist. In this role, you will be responsible for pioneering innovative machine learning techniques to advance our fundamental clinical machine learning and large language model efforts.
You can expect to:
- Design and implement state-of-the-art machine learning techniques to advance Layer Health’s research agenda (in areas such as information extraction, multimodal reasoning, and summarization).
- Propose new agentic methods that tackle fundamental NLP and ML challenges such as modeling over multiple documents, long contexts, multiple modalities, and with limited or noisy labels
- Build foundation models to power the future of clinical information extraction & synthesis, from training through inference.
- Stay up-to-date and actively engage with cutting-edge research in NLP, generative AI, and clinical machine learning.
- Collaborate with the broader engineering team to ship performant products that meet user needs.
- Cultivate and foster a robust and thoughtful R&D culture that drives the company forward.
We look for:
- Exceptional methodological research background and experience, including but not limited to:
- A PhD in computer science/applied mathematics or equivalent research experience, specializing in natural language processing and machine learning.
- High-impact, early-author publications at top peer-reviewed ML journals/conferences.
- Demonstrated record of delivering real-world impact from start to finish - with the ability to design, develop, and ship innovations.
- Strong programming skills and fluency with modern machine learning/LLM stacks (deep learning libraries e.g. PyTorch, Jax).
- Past experience in training/inference of foundation models (billions of parameters, distributed training, familiarity with state-of-the-art techniques).
- A strong communicator who thrives in a customer-focused, fast-paced environment.
- An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team.
- Past experience in healthcare of life sciences is a plus, but not required.
- We are a Boston-based company, and expect employees to meet regularly in-person in Boston (employees from Boston, NYC, or east coast are welcome).
Expected compensation range for this role is $200,000-250,000, in addition to stock options. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made.
If you are excited about this role, we encourage you to apply even if you don't feel that you meet every single requirement. We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways. We welcome diverse perspectives, rigorous thinking, and fearlessness in challenging the status quo.
Layer Health is committed to fostering an environment of inclusion that is free from discrimination. We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law.
Join us and help us transform healthcare with AI.
As published by Layer Health. Applications are handled on their site.
Skills this posting mentions
About Layer Health
Layer Health is a healthcare AI company spun out of MIT and backed by GV (Google Ventures), Define Ventures, Flare Capital Partners, General Catalyst, MultiCare Health System and Froedtert Health. We are solving the information problem in healthcare.
All 18 openings at Layer HealthOne click, then it is written
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01Drop your resume
A PDF or a LinkedIn URL. About a minute, once.
02I rank the openings
Every weekday morning, the live catalog scored against your history. Up to 20 worth your time, not two hundred links.
03Each one is written up
Resume rewritten for the posting, a cover letter, a fit score. Press send, or let me fill in the form.
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Paste your career in once. Every application after that is written for you.
Drop a resume or a LinkedIn URL. I rank the live openings against it, rewrite the resume and write a cover letter for the best of them, and fill in the employer's form when you press the button. You read, you decide what goes out.
01Drop your resume
A PDF or a LinkedIn URL. About a minute, once.
02I rank the openings
Every weekday morning, the live catalog scored against your history. Up to 20 worth your time, not two hundred links.
03Each one is written up
Resume rewritten for the posting, a cover letter, a fit score. Press send, or let me fill in the form.
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