Senior Software Engineer, Machine Learning Infrastructure

at  Prenuvo

ABOUT US

At Prenuvo, we are on a mission to flip the paradigm from reactive “sick-care” to proactive health care. Our award-winning whole body scan is fast (under 1 hour), safe (MRI has no ionizing radiation), and non-invasive (no contrast). Our unique integrated stack of optimized hardware, software, and increasingly AI, coupled with the patient-centric experience of our clinics across North America, have allowed us to lead the charge against “we caught it too late again.” Prenuvo operates preventative screening clinics in Vancouver, Silicon Valley, Dallas, Houston, Boca Raton, Minneapolis, Los Angeles, New York City, Buffalo, Bethesda, Chicago, and Atlanta, with more clinics in North America and internationally in the next 2 years.
We are looking for a Senior Software Engineer with experience in working on machine learning infrastructure and tooling, to help streamline and scale our data science and machine learning workflows.
Help reshape the world through proactive healthcare while working with cutting-edge technology and high performing teams with deep expertise - join us to make a difference in people’s lives!

OUR VALUES

First: we are Pioneers

• Transforming healthcare requires divergent thinking, bias for action, disciplined experimentation, and consistent grit and determination to maintain momentum. This journey is as challenging as it is rewarding.

Second: we are Platform-Builders

• We’re always building foundations that allow us to achieve tomorrow more than we did today. We never lose sight of what’s ahead – in a mindset of ownership and duty to our mission.

Above all: we are Patients

• We could all be the next person who walks through our very doors, seeking clarity or peace of mind. We are proud of our impact on our patients’ lives, and we won’t stop till everyone can benefit from our work.

• Lead the design and implementation of secure, scalable, and reliable ML infrastructure, focusing on data ingestion, training and inference pipelines, active learning, and reproducible data transformations.
• Design and manage CI/CD pipelines for ML training, development, and deployment workflows.
• Package, test, build, and deploy containers and ML artifacts to ensure efficient, clean, and reproducible ML workflows.
• Establish best practices for automation, monitoring, and scaling of ML lifecycle steps.
• Collaborate with ML scientists, engineers, and radiologists to turn research prototypes into production-ready systems.
• Produce and maintain documentation to support regulatory clearances (FDA, IDE, etc.).