Senior Machine Learning Engineer

VideaHealth

Senior Machine Learning Engineer developing AI solutions for healthcare with innovative ML systems and pipelines. Collaborating across teams to enhance patient care and operational performance in clinical settings.

Posted 6/17/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesSenior💰 $170,000 - $205,000 per yearWebsite

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Design, build, and deploy production ML systems for clinical decision support and operational insight, applying deep expertise from your area of specialty.
Develop ML pipelines that integrate structured clinical or EHR data with outputs from computer vision models to power downstream applications.
Ensure the calibration, robustness, and interpretability of deployed models, including clear clinician-facing explanations where relevant.
Implement monitoring, drift detection, evaluation protocols, and retraining or update workflows for production systems.
Partner cross-functionally with product, engineering, clinical, and compliance teams to define requirements and integrate models into live workflows.
Contribute to regulatory documentation for ML systems (data descriptions, validation reports, model versioning).
Mentor engineers and help establish best practices for applied ML and experimentation.

Requirements

What you’ll need

4+ years building and deploying machine learning systems in production, ideally with real-world or clinical data.
Deep, demonstrable expertise in at least one area of ML engineering, such as predictive and tabular modeling, multimodal systems, training and inference infrastructure, or model evaluation and reliability, along with the breadth to contribute across the stack.
Strong development skills in Python with testing, CI/CD, and collaborative coding practices.
Exceptional critical thinking and problem decomposition. Able to turn ambiguous clinical or business questions into measurable hypotheses, design sound experiments, and reason clearly about trade-offs between accuracy, reliability, interpretability, and operational impact.
Familiarity with production ML practices, including monitoring data drift, performance over time, and model health.
Excellent communication skills and a collaborative, product-oriented mindset.
M.S. or Ph.D. in a relevant technical field is preferred.
Experience with healthcare data or regulated ML systems is preferred.
Background in multimodal or stacked models, especially combining CV outputs with tabular data is preferred.
Familiarity with survival analysis, time-series, or longitudinal modeling is preferred.
Open-source contributions or published work in applied ML is preferred.
Prior leadership or mentorship experience is preferred.

Benefits

Comp & perks

Competitive pay, equity and benefits (flexible PTO)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learning systemsML pipelinespredictive modelingtabular modelingmultimodal systemsmodel evaluationPythonCI/CDmonitoring data driftsurvival analysis

Soft Skills

critical thinkingproblem decompositioncommunication skillscollaborative mindsetmentorshipbest practices establishment

Certifications

M.S.Ph.D.