Implement efficient query layers using vector stores and graph databases. * Develop interpretable embedding diagnostics, attribution tools, and graph-based audits to enable safe clinical use.
Implement efficient query layers using vector stores and graph databases. * Develop interpretable embedding diagnostics, attribution tools, and graph-based audits to enable safe clinical use.
Senior Principal AI/ML Engineer - Research Sovereign AI
Rochester, MN · On-site
$115K - $158K/yr
Implement efficient query layers using vector stores and graph databases. * Develop interpretable embedding diagnostics, attribution tools, and graph-based audits to enable safe clinical use.
Senior Principal AI/ML Engineer - Research Sovereign AI
Rochester, MN · On-site
$115K - $158K/yr
Implement efficient query layers using vector stores and graph databases. * Develop interpretable embedding diagnostics, attribution tools, and graph-based audits to enable safe clinical use.
Senior Principal AI/ML Engineer - Research Sovereign AI
Rochester, MN · On-site
$115K - $158K/yr
Implement efficient query layers using vector stores and graph databases. * Develop interpretable embedding diagnostics, attribution tools, and graph-based audits to enable safe clinical use.
Senior Principal AI/ML Engineer - Research Sovereign AI
Rochester, MN · On-site
$115K - $158K/yr
Implement efficient query layers using vector stores and graph databases. * Develop interpretable embedding diagnostics, attribution tools, and graph-based audits to enable safe clinical use.
Senior Principal AI/ML Engineer - Research Sovereign AI
Rochester, MN · On-site
$127K - $175K/yr
Implement efficient query layers using vector stores and graph databases. * Develop interpretable embedding diagnostics, attribution tools, and graph-based audits to enable safe clinical use.
Senior Principal AI/ML Engineer - Research Sovereign AI
Rochester, MN · On-site
$127K - $175K/yr
Implement efficient query layers using vector stores and graph databases. * Develop interpretable embedding diagnostics, attribution tools, and graph-based audits to enable safe clinical use.
Vector Databases information
What are vector databases?
Vector databases are specialized databases designed to store, manage, and search high-dimensional vector data, which is commonly generated from machine learning models, such as embeddings from natural language processing or image recognition. They enable efficient similarity search operations, such as finding the most similar items to a given query vector, which is essential for applications like recommendation systems, semantic search, and AI-powered search engines. Unlike traditional databases that handle structured or unstructured data, vector databases are optimized for fast and scalable similarity searches on large datasets of vectors.
What are some common challenges faced when working with vector databases, and how can they be addressed?
Professionals working with vector databases often encounter challenges such as efficiently scaling to handle large datasets, ensuring low-latency similarity searches, and integrating the database with machine learning pipelines. To address these, teams typically implement distributed architectures, fine-tune indexing strategies, and collaborate closely with data engineers and machine learning specialists. Staying updated with the latest developments in vector database technologies and maintaining clear communication with cross-functional teams are also key to overcoming these challenges.
What is the difference between Vector Databases vs Data Engineers?
| Aspect | Vector Databases | Data Engineers |
|---|---|---|
| Required Skills | Database management, data modeling, query optimization | Data pipeline development, ETL processes, programming |
| Work Environment | Data storage systems, AI/ML projects, cloud platforms | Data infrastructure, cloud environments, big data tools |
| Industry Usage | AI, machine learning, recommendation systems | Data integration, analytics, data architecture |
While Vector Databases focus on storing and querying high-dimensional vector data for AI applications, Data Engineers build and maintain data pipelines and infrastructure to support data analysis and machine learning workflows. Both roles are essential in data-driven industries but serve different functions within the data ecosystem.
What are the key skills and qualifications needed to thrive as a Vector Database Engineer, and why are they important?
Success as a Vector Database Engineer requires a strong background in computer science, database management, and experience with machine learning or AI-driven data systems. Familiarity with vector database platforms (such as Pinecone, Milvus, or Weaviate), cloud infrastructure, and proficiency in languages like Python are typically expected. Strong problem-solving skills, effective communication, and the ability to work cross-functionally help engineers stand out. These competencies are vital to efficiently design, deploy, and maintain scalable vector search solutions that power modern AI applications.
What cities near Kasson, MN are hiring for Vector Databases jobs? Cities near Kasson, MN with the most Vector Databases job openings:
$175K - $263K/yr
Full-time
Medical, Dental, Vision, Retirement
Posted 29 days ago
Mayo Clinic rating
7.9
Based on 682 frontline employees who took The Breakroom Quiz
107th of 875 rated healthcare providers
Job description
Why Mayo Clinic
Mayo Clinic is top-ranked in more specialties than any other care provider according to U.S. News & World Report. As we work together to put the needs of the patient first, we are also dedicated to our employees, investing in competitive compensation and comprehensive benefit plans - to take care of you and your family, now and in the future. And with continuing education and advancement opportunities at every turn, you can build a long, successful career with Mayo Clinic.
Benefits Highlights
- Medical: Multiple plan options.
- Dental: Delta Dental or reimbursement account for flexible coverage.
- Vision: Affordable plan with national network.
- Pre-Tax Savings: HSA and FSAs for eligible expenses.
- Retirement: Competitive retirement package to secure your future.
Responsibilities
Position Summary
The Senior Principal AI/ML Engineer for AI Representation & EMR Vectorization is the senior technical leader and lead scientist responsible for architecting Mayo Clinic's unified multimodal EMR representation layer. This role defines and builds the scientific substrate used by foundational models, clinical agents, and research applications. The individual serves as a hands-on expert and player-coach, guiding technical strategy while contributing directly to model development, graph construction, and representation science. Over time, this position will build and lead a specialized team.
Key Responsibilities
Scientific & Technical Leadership
- Design and implement Mayo's multimodal EMR representation AI architecture, including text, imaging, waveform, structured data, temporal sequences, and multi-visit trajectories.
- Develop graph-based representations and knowledge graphs linking patients, events, attributes, clinical concepts, and embeddings.
- Integrate graph reasoning, vector similarity search, and hybrid vector-graph pipelines for retrieval-augmented models and agentic reasoning.
- Define standards for temporal modeling, drift-aware embeddings, and sequence alignment across encounters.
Hands-On Modeling & Engineering
- Build large-scale embedding pipelines using transformer-based models, contrastive learning, graph neural networks, and hybrid architectures.
- Implement efficient query layers using vector stores and graph databases.
- Develop interpretable embedding diagnostics, attribution tools, and graph-based audits to enable safe clinical use.
- Explore and implement methods for explaining similarity, graph traversals, temporal evolution, and patient-neighborhood reasoning.
Cross-functional Collaboration
- Work with AI researchers on specialty-specific embeddings, representation refinement, and research prototypes.
- Collaborate with clinicians to operationalize clinically meaningful features, phenotypes, and longitudinal concepts.
- Provide scientific input to the Foundational Model Science Program to ensure alignment between representations and model architectures.
Team Leadership
- Serve as founding technical lead of the Reasoning EMR Representation team.
- Mentor junior scientists and engineers; build a future team specializing in representation learning and graph-based reasoning.
Qualifications
Required
- Master's in Computer Science, Machine Learning, Biomedical Engineering, or related field. 9 years of relevant experience, or a bachelor's degree with 11 years of relevant experience.
- Extensive (9+ years) experience applying AI and machine learning in production healthcare environments or similar highly regulated or technology focused industries, showcasing an acute understanding of healthcare technology.
- Hands-on expertise with graph databases, and knowledge graph construction.
- Strong experience with transformer-based models, contrastive learning, and temporal modeling.
Experience designing or deploying vector search systems and hybrid vector-graph reasoning pipelines.
Preferred
- PhD or Master's in Computer Science, Machine Learning, Biomedical Engineering, or related field.
- 10+ years experience building production ML systems, including multimodal architectures and representation learning.
- Experience with EMR data, healthcare multimodality, or clinical data integration.
- Experience building patient similarity models, temporal embedding systems, or phenotype discovery pipelines.
- Strong background in explainability, causality, or interpretable ML.
- Prior experience in a player-coach or team-lead role.
Exemption Status
Exempt
Compensation Detail
Education, experience and tenure may be considered along with internal equity when job offers are extended.; $175,406-263,099 annually
Benefits Eligible
Yes
Schedule
Full Time
Hours/Pay Period
80
Schedule Details
Monday - Friday regular day hours
Weekend Schedule
none expected
International Assignment
No
Site Description
Just as our reputation has spread beyond our Minnesota roots, so have our locations. Today, our employees are located at our three major campuses in Phoenix/Scottsdale, Arizona, Jacksonville, Florida, Rochester, Minnesota, and at Mayo Clinic Health System campuses throughout Midwestern communities, and at our international locations. Each Mayo Clinic location is a special place where our employees thrive in both their work and personal lives. Learn more about what each unique Mayo Clinic campus has to offer, and where your best fit is.
Equal Opportunity
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, protected veteran status or disability status. Learn more about the 'EOE is the Law'. Mayo Clinic participates in E-Verify and may provide the Social Security Administration and, if necessary, the Department of Homeland Security with information from each new employee's Form I-9 to confirm work authorization.
Recruiter
Jill SquierQualifications:
Required
- Master's in Computer Science, Machine Learning, Biomedical Engineering, or related field. 9 years of relevant experience, or a bachelor's degree with 11 years of relevant experience.
- Extensive (9+ years) experience applying AI and machine learning in production healthcare environments or similar highly regulated or technology focused industries, showcasing an acute understanding of healthcare technology.
- Hands-on expertise with graph databases, and knowledge graph construction.
- Strong experience with transformer-based models, contrastive learning, and temporal modeling.
Experience designing or deploying vector search systems and hybrid vector-graph reasoning pipelines.
Preferred
- PhD or Master's in Computer Science, Machine Learning, Biomedical Engineering, or related field.
- 10+ years experience building production ML systems, including multimodal architectures and representation learning.
- Experience with EMR data, healthcare multimodality, or clinical data integration.
- Experience building patient similarity models, temporal embedding systems, or phenotype discovery pipelines.
- Strong background in explainability, causality, or interpretable ML.
- Prior experience in a player-coach or team-lead role.
What Mayo Clinic employees say
Pay
Benefits
Hours and flexibility
Workplace
Get the full story on Breakroom
About Mayo Clinic
Sourced by ZipRecruiter
Mayo Clinic is the largest integrated, not-for-profit medical group practice in the world. We're building the future, one where the best possible care is available to everyone — and more people can heal at home. Our relentless research turns into earlier diagnoses and new cures. That's how we inspire hope in those who need it most. At Mayo Clinic, experts work together to solve the most challenging unmet needs of patients. Our history of innovation dates back almost 150 years, when brothers Will and Charlie Mayo pioneered an integrated, team-based approach to medicine. Today, that trailblazing spirit drives innovations like Mayo Clinic Platform — which powers new technologies to change how care is delivered to all.
Industry
Hospitals
Company size
10,000+ Employees
Headquarters location
Rochester, MN, US
Year founded
1919