Videa is a cutting-edge AI-powered solution for dentistry, developed by a team of seasoned leaders, engineers, AI scientists, and clinicians spun out of MIT. Our vision is to be the first company to diagnose a billion people globally. Our product is already used by thousands of dental clinicians to enhance the quality of care through faster diagnoses, to increase operating efficiencies, and to improve patient understanding.
About the position: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.
Key Responsibilities: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.
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.
Experience with healthcare data or regulated ML systems.
Background in multimodal or stacked models, especially 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 or mentorship experience
What We Offer
Fast paced and collaborative work culture in which you can gain experience, grow your technical skills and work on a wide variety of challenges over your time with us
Competitive pay, equity and benefits (flexible PTO)
Agile organization where being senior translates to being a mentor and role model for others. We lead by example.
Technical challenges on the leading edge of innovation where software and machine learning intersect.
Videa is supported by some of the best investors in the world, having raised over $67M in Venture Capital from Tier 1 investors such as Spark Capital (Twitter, SnapChat, SmileDirectClub), Zetta Venture (Kaggle), and Pillar VC (PillPack), as well as angel investors such as Frederic Kerrest (Co-founder of Okta). Our work has been featured in TechCrunch, Wall Street Journal, and many other outlets.
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!
Skills Required
- 4+ years building and deploying machine learning systems in production
- Deep expertise in at least one area of ML engineering (predictive/tabular, multimodal, training/inference infrastructure, or model evaluation/reliability)
- Strong development skills in Python with testing, CI/CD, and collaborative coding practices
- Familiarity with production ML practices including monitoring data drift, performance over time, and model health
- Exceptional critical thinking; ability to design experiments and reason about trade-offs between accuracy, reliability, and interpretability
- Excellent communication skills and a collaborative, product-oriented mindset
- Experience with real-world or clinical data / EHR datasets
- M.S. or Ph.D. in a relevant technical field
- Experience with healthcare data or regulated ML systems
- Background in multimodal or 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 or mentorship experience
Videa Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Videa and has not been reviewed or approved by Videa.
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Leave & Time Off Breadth — Unlimited PTO is explicitly stated on the careers page and repeated across job postings, signaling broad time-off flexibility. Consistent language across third-party listings reinforces that this policy is standard across roles.
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Healthcare Strength — Company materials highlight 100% employer-paid dental and vision plus employer contributions to medical coverage. This emphasis points to strong support in core health benefits.
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Affordable Benefits — Fully paid dental and vision reduce employee out-of-pocket costs for those coverages. Employer medical contributions further suggest a lighter premium burden compared with typical employee-paid plans.
Videa Insights
What We Do
VideaHealth is transforming Dentistry by combining advanced AI of x-ray images with integrated software that dramatically improves diagnoses and streamlines insurance claims processing. Patients get better recommendations, dentists learn faster, and insurers reduce fraud, waste and abuse in real time. Our AI factory continuously expands the range of conditions we can detect, including those that affect broader medical risks. Our UI and integration capability makes it easy to get rapid time-to-value for dental practices and insurers.
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