Senior Machine Learning Engineer, Data & Intelligence Products

AcuityMD•Boston, MA

1d•Remote

About The Position

AcuityMD is a software and data platform that accelerates access to medical technologies. We help MedTech companies understand how their products are used, why customers vary, and identify opportunities for physicians to better serve their patients. Each year, the FDA approves ~6,000 new medical devices. Our solution helps MedTech companies get these products to physicians more effectively so they can improve patient care with the latest technology. We're backed by Benchmark, Redpoint, ICONIQ Growth, and Ajax Health. We're a high-growth AI and Data company scaling rapidly. We are a team of agentic builders who rely heavily on AI development practices to accelerate our ability to deliver value to our customers. We believe in extreme ownership and agency, where every engineer takes responsibility for working throughout the organization to land impact with customers. We're a close-knit team that enables high levels of autonomy by constantly communicating our intentions, sharing problems and learnings, and helping each other learn and grow. In this role, you will help lead the evolution of AcuityMD's core healthcare data assets by applying statistical and machine learning techniques to convert messy, real-world health data into insight about the healthcare market. You will design and ship predictive models and data products, improve the quality and interpretability of existing models, and deliver high-confidence, repeatable intelligence that fuels our MedTech-specific modules. You will identify new approaches and new data sources that extend what our platform can predict and explain. You will work cross-functionally with engineering, product, and commercial teams.

Requirements

6+ years of experience in machine learning roles building and shipping statistical or machine learning models into a production environment, ideally as part of product teams delivering to external customers
Strong foundations in applied statistics and ML — regression, classification, forecasting, clustering, experimental design, and model evaluation — and you know when each is the right tool.
Instinctively build using agentic tools (Claude Code, Codex, etc) and are invested in pushing the boundaries of what is possible with agentic development
Can translate technical recommendations and model behavior clearly and concisely for non-technical product, commercial, and customer audiences
Hands-on experience merging and blending messy, real-world datasets — time-series, geospatial, demographic, etc — and thrive on extracting signal from noise
Comfortable working in modern cloud data warehouses with SQL to prepare data for modeling, and can collaborate effectively with data engineers on production pipelines
Fluent in Python's data and ML stack and opinionated about your preferred approaches, techniques, or model implementations

Nice To Haves

Experience with healthcare datasets, such as medical insurance claims, prescriptions, EHR/EMR, lab test results, or patient demographic data, is a strong plus
Eligible work permit in the USA

Responsibilities

Design, train, and validate predictive and statistical models that turn noisy healthcare data into reliable intelligence products used by MedTech commercial teams
Frame open-ended business questions as modeling problems — selecting the right approach (classification, regression, clustering, causal inference, ensembles, etc), defining success metrics, and quantifying uncertainty
Engineer features and conduct applied research across time-series, geospatial, demographic, insurance claims, and more datasets, to improve the coverage and signal quality of our core data assets
Own the full model lifecycle: exploratory analysis, baseline modeling, experimentation, validation, deployment, and post-launch monitoring for drift and performance
Partner with product managers and cross-functional stakeholders to translate customer problems into model-backed product features and to shape the roadmap
Provide technical leadership and mentorship on statistical and ML methodology for engineers and analysts across the Data organization and across all of AcuityMD
Document models, assumptions, and data contracts so results are interpretable and reproducible for internal and external audiences

Benefits

Highly competitive cash compensation
Equity
Reimbursements for relevant learning and up-skilling opportunities (Learning Budget)
Remote work flexibility for employees in the US
Work-from-home stipend
Generous time off and flexible hours (Flexible PTO)
100% paid health, dental, and vision plans for all employees
75% paid health, dental, and vision plans for dependents
$1,000 to invest in remote office equipment and WiFi reimbursement (Home Office Stipend)
Optional Team Retreats
8-16 weeks of fully-paid, flexible parental leave