Lead Engineer, Healthcare Data Operations and Strategy

At NVIDIA, we're building the platforms to accelerate the healthcare applications of tomorrow. Software, Hardware, as well as data. We're looking for dedicated contributors who know how to focus on the latter. This is an opportunity to influence the direction of NVIDIA research, engineering, and ultimately the products which our customers build.

This role leads healthcare data operations and guides its strategy. You decide what to build next and collaborate with community partners to build it. You also establish the MLOps backbone that makes each program a durable, growing asset. You connect leading clinicians, academic researchers, medtech industry partners, and NVIDIA's engineering and product teams.

What You'll Be Doing:

• Define a portfolio strategy and selection methodology for NVIDIA's healthcare data programs with key collaborators. Prioritize modalities, clinical domains, and partner cohorts based on scientific and market impact, downstream model value, partner readiness, and technical feasibility.

• Drive the tactical execution of new healthcare data collaborations end-to-end: prioritizing, data contribution agreements and licensing, contribution standards, release planning, and public launch.

• Architect and build our healthcare data MLOps platform that ingests, curates, validates, governs, and serves multi-institution healthcare data at scale. Combine NVIDIA's internal tooling with outstanding external systems when appropriate.

• Partner directly with NVIDIA healthcare and model training teams (e.g., GR00T, Cosmos) to ensure data programs are sequenced and crafted to feed the highest-priority needs.

• Establish data quality, provenance, de-identification, and governance standards that scale across modalities and meet the regulatory and compliance expectations of global clinical partners.

What We Need to See:

• 12+ years working with healthcare data — building datasets, running data programs, or leading MLOps workflows in a healthcare or medtech setting.

• Strong technical proficiency across the healthcare data lifecycle: ingestion, curation, annotation, de-identification, governance (HIPAA, GDPR, IRB workflows), and serving for training and evaluation.

• Hands-on experience with MLOps tooling — data lakes/lakehouses, dataset versioning (e.g., Hugging Face Datasets, LakeFS, DVC), workflow orchestration, validation frameworks — and a clear point of view on when to build versus integrate.

• Familiarity operating at the intersection of strategy, partnerships, and engineering — able to set portfolio direction one day and review schema choices or pipeline architectures the next.

• BS or higher in Computer Science, Biomedical Engineering, Computational Biology, or a related technical field, or equivalent experience.

Ways to Stand Out from the Crowd:

• Direct healthcare industry experience — including familiarity with how device data is generated, retained, and released.

• Track record of launching publicly released, commercially usable healthcare datasets.

• Experience standing up data infrastructure for foundation model training, including multi-modal sensor data.

• Deep relationships across the global clinical AI community (MedTech or biopharma), and a history of converting those relationships into shipped artifacts.

• Familiarity with NVIDIA platforms relevant to healthcare AI — Holoscan, BioNeMo, Cosmos, Isaac, NeMo Data Designer, or Omniverse.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.Applications for this job will be accepted at least until May 12, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.