Staff Software Engineer / Tech Lead - ML Infrastructure

4w1 month ago

HeartFlow, Inc.

San Francisco, US · Full-time · $190,000 – $250,000

About this role

Heartflow is a medical technology company advancing the diagnosis and management of coronary artery disease using AI-driven, non-invasive cardiac tests. We are looking for a Staff Software Engineer to act as the technical anchor for a small, focused team working on Data and ML infrastructure.

In this player-coach role, you will set the architectural vision and guide day-to-day technical execution while remaining deeply hands-on. You will write high-performance Python code, use frameworks like Ray, and deploy complex ML algorithms into highly available cloud environments.

Our team builds Data & ML infrastructure to simplify developing, evaluating, and deploying algorithms on massive medical imaging datasets, using self-serve interfaces and robust documentation. We value breaking down complex architectural challenges into well-communicated solutions using first-principles thinking.

This position gives you the platform to lead technically, whether you are a seasoned tech lead or a Staff IC ready to step up. Your work will directly support large-scale machine learning applied to precision healthcare, impacting patient outcomes worldwide.

Requirements

8+ years of professional software engineering experience.
Strong proficiency in Python and experience with high-performance computing.
Experience with distributed computing frameworks such as Ray.
Proven ability to architect and deploy ML infrastructure in cloud environments (AWS, GCP, Azure).
Experience managing large-scale, unstructured datasets in cloud-data systems.
Demonstrated technical leadership and mentoring skills in a player-coach capacity.

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.

Benefits

Hybrid work schedule from San Francisco office.
Opportunity to contribute to a product used for over 500,000 patients worldwide.
Work with a company that has received international recognition for healthcare innovation.
Join a team clearing regulatory pathways in US, UK, Europe, Japan, and Canada.