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Software Engineer - Engineering Productivity

Cloverhealth

Remote · US

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About this role

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 Engineer, 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.

As a Software Engineer, Engineering Productivity, you will:

• Design and champion internal Quality Programmes: 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 (GitHub, 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.

Success in this role looks like:

• You operate as a high-leverage Individual Contributor (IC) and reliability champion, effectively negotiating with engineering pods and leadership to roll out and adopt critical quality processes across the entire SDLC.

• You operate with a strong bias for action—designing automated systems that actively enforce our reliability thresholds and comfortably holding the line on quality standards when deployments are at risk.

• You have made engineering health measurable. Delivery efficiency and software quality are no longer subjective conversations — they are numbers on a dashboard that leadership and pod leads both trust and act on.

• You have delivered impactful projects that advance our engineering roadmap, reducing toil or streamlining operations.

• You have built trusted partnerships across engineering teams and contribute to the success of their projects where needed.

• You proactively identify opportunities to improve how we build, deploy, and scale – bringing a thoughtful, systems-oriented lens to everything from infrastructure to process.

You should get in touch if:

• You have 5+ years of experience in software with proficiency in one or more common languages (e.g., Python, Go), and are comfortable working across different technical systems and concerns.

• You have worked at a systems level with modern developer infrastructure and production telemetry, and can query, combine, and process data from tool APIs — ours are GitHub, Linear, GCP, Sentry, Grafana and incident.io, but equivalents like GitLab, Jira or Datadog count just as much. You have built the mechanisms that make engineering health visible, not just consumed someone else's dashboard.

• You have personally instrumented hard quality metrics — several of Change Failure Rate, Deployment Frequency, Lead Time for Changes, Time to Restore, Regression Rate, Release Failure Rate, PR-failure rate, code review depth, or SLIs/SLOs and error budgets — rather than just referenced them.

• You have a strong product-oriented mindset, care about outcomes over output, and want to understand the "why" behind what you build to connect your work to business impact.

• You have experience building and refa

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