Staff Software Engineer / Tech Lead, ML Infrastructure

Heartflow•San Francisco, CA

4d•Hybrid

About The Position

Heartflow is a medical technology company advancing the diagnosis and management of coronary artery disease, the #1 cause of death worldwide, using cutting-edge technology. The flagship product—an AI-driven, non-invasive cardiac test supported by the ACC/AHA Chest Pain Guidelines called the Heartflow FFR CT Analysis—provides a color-coded, 3D model of a patient’s coronary arteries indicating the impact blockages have on blood flow to the heart. Heartflow is the first AI-driven non-invasive integrated heart care solution across the CCTA pathway that helps clinicians identify stenoses in the coronary arteries (RoadMap™Analysis), assess coronary blood flow (FFR CT Analysis), and characterize and quantify coronary atherosclerosis (Plaque Analysis). Our pipeline of products is growing and so is our team; join us in helping to revolutionize precision heartcare. Heartflow is a publicly traded company (HTFL) that has received international recognition for exceptional strides in healthcare innovation, is supported by medical societies around the world, cleared for use in the US, UK, Europe, Japan and Canada, and has been used for more than 500,000 patients worldwide.

Requirements

8+ years of professional software engineering experience, with a strong focus on ML infrastructure, distributed systems, or MLOps.
A history of mentoring peers and leading technical projects, and an excitement to act as the technical anchor for a small team of engineers.
Ability to write clear, well-tested, and scalable code, with high proficiency in Python.
Deep understanding of modern distributed computing architectures and how to optimize heavy compute and cloud-data workloads (AWS, GCP, or Azure).
Familiarity with infrastructure as code (e.g., CDK, Terraform).
Familiarity with modern distributed computing frameworks and table formats like Ray, Kubernetes, and Apache Iceberg.
Knowledge of cross-language bindings for high-performance computing (e.g., C++/Python).

Nice To Haves

Prior experience in the healthcare domain, highly-regulated environments, or handling image-based algorithms is a huge plus.

Responsibilities

Act as the technical lead and mentor for a small, high-impact team of engineers, guiding system design, conducting code reviews, and unblocking technical hurdles.
Write high-performance Python code and utilize frameworks like Ray to architect and maintain large-scale distributed computing platforms for ML training and evaluation.
Spearhead the deployment of complex ML algorithms into highly available, scalable cloud environments, ensuring models run efficiently in production.
Design and integrate robust cloud-data systems to manage the lifecycle of massive, unstructured medical datasets.
Work cross-functionally with researchers and engineers to understand how they develop models, using that understanding to solve their ML training, serving, and production monitoring needs.
Responsibly and securely utilize AI-powered development tools (like coding assistants or LLMs) to accelerate the team's engineering workflows.