Senior Machine Learning Engineer

TL;DR

Senior Machine Learning Engineer (AI/Healthcare): Design, build, and scale production machine learning systems that combine structured clinical data with outputs from core computer vision models for clinical decision support and operational insight. Focus on end-to-end ownership of reliable, interpretable, monitored models that meet medical-grade standards, including calibration, robustness, evaluation, and safe integration into healthcare workflows.

Company

VideaHealth builds an AI-powered dentistry solution used by clinicians to improve diagnosis speed, operational efficiency, and patient understanding.

What you will do

• Design, build, and deploy production ML systems for clinical decision support and operational insight.
• Develop ML pipelines integrating structured clinical/EHR data with computer vision model outputs.
• Ensure calibration, robustness, and interpretability of deployed models, including clinician-facing explanations where relevant.
• Implement monitoring, drift detection, evaluation protocols, and retraining/update workflows.
• Partner with product, engineering, clinical, and compliance teams to define requirements and integrate models into live workflows.
• Contribute to regulatory documentation and mentor engineers on applied ML best practices.

Requirements

• 4+ years building and deploying machine learning systems in production, ideally with real-world or clinical data.
• Deep expertise in at least one ML engineering area (predictive/tabular modeling, multimodal systems, training/inference infrastructure, or model evaluation/reliability) with breadth to contribute across the stack.
• Strong Python development skills with testing, CI/CD, and collaborative coding practices.
• Ability to decompose ambiguous clinical/business questions into measurable hypotheses and design sound experiments balancing accuracy, reliability, interpretability, and operational impact.
• Familiarity with production ML practices including monitoring data drift and performance over time.
• Excellent communication skills and a collaborative, product-oriented mindset.

Nice to have

• M.S. or Ph.D. in a relevant technical field.
• Experience with healthcare data or regulated ML systems.
• Experience with multimodal/stacked models combining CV outputs with tabular data.
• Familiarity with survival analysis, time-series, or longitudinal modeling.
• Open-source contributions or published work in applied ML; prior leadership/mentorship experience.

Culture & Benefits

• Fast-paced, collaborative environment with opportunities to grow technical skills across varied challenges.
• Competitive pay with equity and benefits, including flexible PTO.
• Agile organization where seniority includes mentoring and role-modeling.
• Technical challenges at the intersection of software and machine learning.

Hiring process

• Apply for the opportunity and discuss fit for the role’s ML engineering focus and production ownership expectations.