Software Developer - Engineering Productivity
About The Position
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.
Requirements
- 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.
- 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.
- 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.
- 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.
- Experience building and refactoring complex (often distributed) systems and possess experience with declarative infrastructure as code (IaC) deployment patterns.
- Experience with public cloud platforms such as GCP and/or AWS.
- Experience with load and performance testing (k6, Locust, Gatling, JMeter all welcome), including handling synthetic data safely so test traffic never contaminates real user telemetry.
- AI experience in terms of testing tools and architecture. You know how to build systems with AI agents as your partners, and you possess clear strategies for strictly verifying and validating AI-generated code and infrastructure outputs before they merge.
- Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams and are able to adapt quickly to new challenges and technologies.
Nice To Haves
- Keen to build hands-on experience with load and performance testing (k6, Locust, Gatling, JMeter all welcome), including handling synthetic data safely so test traffic never contaminates real user telemetry.
- AI experience in terms of testing tools and architecture. You know how to build systems with AI agents as your partners, and you possess clear strategies for strictly verifying and validating AI-generated code and infrastructure outputs before they merge.
Responsibilities
- 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.
Benefits
- Competitive base salary
- Equity opportunities
- Performance-based bonus program
- Regular compensation reviews
- Comprehensive group medical coverage (hospitalization, outpatient care, optical services, dental benefits)
- No-Meeting Fridays
- Company holidays
- Access to mental health resources
- Generous annual leave policy
- Remote-first culture
- Learning programs
- Mentorship
- Professional development funding
- Regular performance feedback and reviews
- Reimbursement for office setup expenses
- Flexibility to work from home
- Paid parental leave for all new parents
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
