The ad says“Strong development skills in Python with testing, CI/CD, and collaborative coding practices”
The ad says“Strong development skills in Python with testing, CI/CD, and collaborative coding practices”
Values
The ad says“Familiarity with survival analysis, time-series, or longitudinal modeling”
The ad says“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”
The ad says“Familiarity with production ML practices, including monitoring data drift, performance over time, and model health”
The ad says“Familiarity with production ML practices, including monitoring data drift, performance over time, and model health”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“Familiarity with survival analysis, time-series, or longitudinal modeling”
The ad says“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”
The ad says“Familiarity with survival analysis, time-series, or longitudinal modeling”
The ad says“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”
We’re looking for a Senior Machine Learning Engineer with deep expertise in some area of ML engineering to join our growing ML team and work closely with our software and computer vision teams
This is an opportunity to design, build, and scale machine learning systems that combine structured clinical data with outputs from our core computer vision models to improve patient care and operational performance
You’ll own end-to-end development of production ML systems, integrate them safely into healthcare workflows, and deploy reliable, interpretable, and monitored models that meet medical-grade standards
Depending on your background, that might mean predictive and tabular modeling, multimodal systems, large-scale training and inference infrastructure, model evaluation and reliability, or another specialty where you bring real depth
You’ll work alongside ML scientists, clinical experts, and product engineers to translate real clinical questions into systems that ship and hold up over time
We’re looking for a hands-on builder who’s excited to work with real-world clinical data, get models into production, and own them across their full lifecycle
If you care about impact and want to help define the future of applied AI in healthcare, we’d love to meet you
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
Benefits
Opportunities for Growth
Equity Options
Preferred experience
Excellent communication skills and a collaborative, product-oriented mindset
Familiarity with production ML practices, including monitoring data drift, performance over time, and model health
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
Background in multimodal or stacked models, especially combining CV outputs with tabular data
Familiarity with survival analysis, time-series, or longitudinal modeling
M.S. or Ph.D. in a relevant technical field
Prior leadership or mentorship experience
Experience with healthcare data or regulated ML systems
Open-source contributions or published work in applied ML
If you want to join a breakthrough healthtech company and help accelerate its impact and growth, we encourage you to apply for this exciting opportunity!
