Senior Software Engineer - AWS Cost Optimization

Heartflow•San Francisco, CA

2h•$180,000 - $215,000•Hybrid

About The Position

Heartflow is a medical technology company advancing the diagnosis and management of coronary artery disease. The flagship product is an AI-driven, non-invasive cardiac test called the Heartflow FFRCT Analysis. Heartflow is a publicly traded company (HTFL) that has received international recognition for its healthcare innovation and has been used for over 750,000 patients worldwide. The company is leveraging its deep foundational AI expertise to add fuel to its high-growth fire and is assembling a brand-new, dedicated team to pioneer production-grade Agentic AI systems within healthcare. This role is for a highly analytical software engineer passionate about building scalable tools, optimizing cloud compute infrastructure, and maximizing ROI. The engineer will join the dynamic Infrastructure Platform team to drive a critical initiative ensuring modernized Kubernetes workflows and large-scale data systems are cost-effective. This is a hybrid, high-impact role where you will drive the compute cost strategy during the migration to Kubernetes and architect the transition of the massive data footprint from AWS S3 to highly efficient alternative storage providers. This role requires deep expertise in backend software engineering, cloud financial modeling, containerized compute architecture, and object storage paradigms.

Requirements

8+ years of Software Engineering or Platform Engineering experience, with a proven track record of writing clean, scalable, and maintainable code in Python, Go, or Java.
Deep, hands-on experience with object storage APIs (AWS S3, Backblaze) and designing large-scale data transfer pipelines.
Proven experience building centralized client libraries, SDKs, or developer abstractions.
Experience building programmatic financial models, predicting cloud spend, and performing capacity planning for large-scale cloud migrations.
Ability to translate complex technical architecture changes into clear budgetary impacts.
Expert-level understanding of AWS billing, pricing models, enterprise discount programs, and scaling behaviors.
Strong hands-on understanding of Kubernetes (EKS) compute efficiency, alongside legacy services like Lambda and ECS.
Strong background using Terraform to deploy infrastructure and automations.
Excellent communication and technical leadership skills, with the ability to influence technical decisions across engineering, platform, and finance teams.

Nice To Haves

Experience developing Kubernetes operators or Custom Resource Definitions (CRDs) in Go.
Prior experience driving cost optimization and multi-petabyte data migrations simultaneously.
Experience with KubeCost, Finout, CloudHealth, or similar cloud-native FinOps solutions.
FinOps Foundation Certified Practitioner or Professional certification.
Experience working in a monorepo environment and establishing modern CI/CD practices for shared libraries.

Responsibilities

Architect and execute the large-scale, high-throughput migration of petabytes of data from AWS S3 to lower-cost alternative storage providers (e.g., Backblaze B2).
Design, develop, and maintain robust, centralized client libraries (SDKs) to abstract the underlying storage infrastructure, enabling application teams to seamlessly interact with object storage without vendor lock-in.
Partner closely with platform engineers to ensure the reference architectures for moving legacy workloads (AWS Lambda, ECS) to our target EKS clusters are cost-optimized by design.
Benchmark migrated services, right-size resource requests/limits, and develop strategies for Reserved Instances (RIs), Savings Plans, and Spot Instance utilization.
Develop, maintain, and refine sophisticated programmatic cloud capacity and spend models.
Forecast future infrastructure costs based on our Kubernetes migration roadmap, storage migrations, and traffic patterns, ensuring accurate budgeting and variance analysis in partnership with Finance.
Write custom software, automation scripts, and Kubernetes operators (using Python, Go, etc.) to enforce automated cost-control guardrails, resource tagging, and intelligent scale-down/cleanup for non-production environments.
Manage these guardrails via Infrastructure as Code (Terraform).
Implement and manage advanced cost observability telemetry (e.g., querying the AWS Cost and Usage Report (CUR) programmatically, KubeCost).
Build transparent, granular chargeback reporting into our internal developer platforms to empower individual engineering teams.