5 часов назад
Software Engineer - Engineering Productivity (AI)
146 200 - 190 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Software Engineer - Engineering Productivity (AI) (backend infrastructure and SDLC observability): Building telemetry pipelines, quality gates, load-testing systems, and synthetic-data architecture to improve deployment velocity and software reliability with an accent on platform engineering, production observability, and automated quality metrics. Focus on designing CI/CD enforcement, validating distributed systems under realistic load, and safely verifying AI-generated code and infrastructure.
Location: Remote in the United States or Canada; regular early-morning or evening calls may be required to collaborate with colleagues in Hong Kong.
Base salary: $146,200–$190,000 USD annually, plus equity and performance-based bonus opportunities.
Company
develops Counterpart Assistant, an AI-enabled primary care tool that supports earlier diagnosis and chronic-condition management.
What you will do
- Design and champion internal quality programs and reliability standards across engineering pods.
- Build SDLC observability pipelines using APIs and platforms including GitHub, Linear, GCP, Sentry, Grafana, and incident.io.
- Define and automate reporting for deployment and quality metrics such as change failure rate, deployment frequency, lead time, recovery time, and regression rate.
- Architect shift-left performance, security, and accessibility gates within CI/CD pipelines.
- Build load-testing, synthetic-data, and production-canary systems that isolate test traffic from real user telemetry.
- Consolidate legacy testing infrastructure and use AI-assisted tooling to accelerate testing and infrastructure development.
Requirements
- 5+ years of software engineering experience with proficiency in a common language such as Python or Go.
- Experience with developer infrastructure, production telemetry, tool APIs, and engineering health metrics.
- Experience building or refactoring complex distributed systems and using declarative infrastructure-as-code deployment patterns.
- Experience with public cloud platforms such as GCP and/or AWS.
- Hands-on experience or strong interest in load and performance testing with tools such as k6, Locust, Gatling, or JMeter, including safe synthetic-data handling.
- Experience using AI testing tools and architectures, with strategies for validating AI-generated code and infrastructure before merging.
Nice to have
- Experience with equivalent tools such as GitLab, Jira, or Datadog.
- Experience with multiple load-testing frameworks, including k6, Locust, Gatling, or JMeter.
Culture & Benefits
- Remote-first work environment with flexibility to work from home and collaborate across global time zones.
- Medical, hospitalization, outpatient, optical, and dental coverage for employees and families.
- Company holidays, No-Meeting Fridays, mental-health resources, generous annual leave, and paid parental leave.
- Learning programs, mentorship, professional-development funding, and regular performance feedback.
- Office setup reimbursement, equity opportunities, performance-based bonuses, and regular compensation reviews.
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