Location: Hybrid / San Francisco, CA

What's the role?

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. Your work will directly support large-scale machine learning applied to precision healthcare.

This is a highly technical, player-coach role. You will set the architectural vision and guide the day-to-day technical execution of the team, remaining deeply hands-on in the codebase. Whether you are already a seasoned tech lead or a Staff Individual Contributor (IC) ready to step up and guide a sub-team of engineers, this position gives you the platform to lead technically.

This is a full-time position operating on a hybrid schedule out of our San Francisco office.

What's this team like at Heartflow?

We build Data & ML infrastructure to simplify developing, evaluating, and deploying algorithms on massive medical imaging datasets. Our team owns critical platforms across the stack: data systems focused on curation and analytics, as well as our core ML environment for both training and inference.

We design our infrastructure to not just be highly performant, but also easy to use. We are proponents of self-serve interfaces and robust user documentation. Our team tackles challenging problems using first-principles thinking, and we greatly value the ability to break down complex architectural challenges into digestible, well-communicated solutions.

What does the day-to-day look like?

• 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.

Qualities that will help you thrive in this role are:

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

Salary Range: $190,000 - $250,000 base, plus cash bonus and equity.