Layer Health

Engineering Manager, Machine Learning

Boston or NYC

Sponsorship not specified$250k-$275kDetected 217 days ago

PythonMachine LearningData EngineeringNLPLLMsA/B TestingPatient CareResearchLeadershipCommunicationCollaboration

About the role

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.

Responsibilities

Act as a player-coach: you'll set direction while remaining actively involved in design, experimentation, and implementation.
Drive the development of end-to-end ML systems-from shaping ambiguous problems into clear model requirements, training datasets, evaluation frameworks, and model architectures, to supporting reliable production deployment.
Help craft and drive the ML technical agenda in partnership with engineering, research, and product leadership, ensuring the roadmap aligns with company goals and high-impact opportunities.
Lead rigorous experimentation and model evaluation, ensuring our LLM systems meet clinical-grade performance and reliability requirements.
Work closely with our engineering team to integrate and scale models in production, optimize inference efficiency, and maintain strong observability and monitoring.
Communicate technical work clearly to cross-functional partners and leadership, translating ML developments into strategic implications for the business and product.
Deep ML expertise: model development, training workflows, data pipeline design, evaluation methodology, and production deployment.

Requirements

Degree in computer science, mathematics, physics, or a related field.
Strong Python fluency and experience with modern ML tooling and infrastructure.

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.

Benefits

Establish and champion best practices in modeling, code quality, reproducibility, and experiment design-helping define "what incredible looks like" for ML at an early-stage, mission-driven health tech company.
7+ years of hands-on ML experience, ideally including LLMs or NLP; healthcare exposure is a plus but not required.

Company info

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.
We're currently looking to hire our first ML Engineering Manager to help mentor our team and oversee ML strategy. This is a hybrid role in our Boston or NYC office
you will work closely with our engineers, ML scientists, and product teams to enable our team to build ML-native enterprise platforms, ensuring scalability, efficiency, and reliability.
Here's a collection of articles about our product, mission, recent funding round, etc.
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.
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.
We're currently looking to hire our first ML Engineering Manager to help mentor our team and oversee ML strategy.
This is a hybrid role in our Boston or NYC office; you will work closely with our engineers, ML scientists, and product teams to enable our team to build ML-native enterprise platforms, ensuring scalability, efficiency, and reliability.
Provide technical leadership and management to a small, high-leverage team of ML Engineers, Research Engineers, and ML/Data Scientists. Act as a player-coach: you'll set direction while remaining actively involved in design, experimentation, and implementation.
Recruit, cultivate, and inspire the next generation of technical talent as the team grows.
What we look for
2+ years of technical leadership experience (formal or informal) where you've guided teams, set direction, and mentored others and 1 or more years formal management experience.

Equal opportunity

We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need.

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This listing is sourced directly from Layer Health's careers page and normalized into a canonical job model.