Software Developer - Engineering Productivity

At Counterpart Health, we are transforming healthcare and improving patient care with our innovative primary care tool, Counterpart Assistant. By supporting Primary Care Physicians (PCPs), we are able to deliver improved outcomes to our patients at a lower cost through early diagnosis and longitudinal care management of chronic conditions. We value diversity — in backgrounds and in experiences. Healthcare is a universal concern, and we need people from all backgrounds and swaths of life to help build the future of healthcare.

Clover’s engineering team is empathetic, caring, and supportive.

We are looking for a Software Developer, Engineering Productivity with expertise in backend infrastructure, platform engineering, and SDLC observability to join our globally distributed engineering team. You won’t just write code to spec — you’ll understand the business problem, engage with stakeholders, and shape the solution. In this role, your customers are our own engineers: you will act as a high-leverage technical bridge between Quality and Eng Core, building the automated safety nets and engineering telemetry required to increase deployment velocity while driving code-related incidents to zero.

Our engineering team is spread across time zones, and this role works closely with colleagues in Hong Kong. In practice that means regular early-morning or evening calls — we keep the load shared fairly, but comfort with that rhythm matters for this role.

• Design and champion internal Quality Programms: Drive engineering-wide process changes and ensure pods adopt new reliability standards without friction. You will operate with a product-owner mindset to define and evangelize the internal reliability roadmap.
• Build SDLC observability pipelines: Harness the APIs across our existing stack (Git Hub, Linear, GCP, Sentry, Grafana, incident.io) to collect, aggregate, and visualize engineering and quality telemetry — delivering self-service dashboards that give Engineering Managers and pod leads clear visibility into delivery efficiency and SDLC bottlenecks.
• Define and enforce hard metrics: Establish and automate reporting for the Key Quality Indicators that objectively describe deployment health — Change Failure Rate, Deployment Frequency, Lead Time for Changes, Time to Restore, Regression Rate, Release Failure Rate, PR-failure rate, and code review depth — creating a quantifiable baseline for platform reliability.
• Architect Shift-Left Pipeline Gates: Weave automated Performance, Security, and Accessibility checks directly into the CI/CD pipeline at the PR level in tight partnership with Eng Core.
• Build Real-World Load Testing: Shift load and performance testing left for every customer onboarding, validating real-world assumptions using tools such as k6, Locust, Gatling, or JMeter.
• Engineer Synthetic Data & Production Canaries: Build the architecture for safe synthetic data injection to unblock heavy load-testing and live-production canaries, strictly isolating test data from authentic user telemetry.
• Leverage Generative AI Tooling: Actively utilize AI assistants (e.g., Gemini, Claude, Cursor, Codex) to accelerate the development of testing frameworks, automate infrastructure code, and design advanced testing architectures.
• Drive Tooling Consolidation: Lead the technical migration away from expensive, legacy testing infrastructure to a unified, AI-supported automation stack — maximizing the value of the platforms we already have rather than introducing unnecessary vendor complexity.
• Help define and maintain development practices: Enable fast iteration while ensuring quality, including writing tests and documenting key implementations.