Job description

As a key member of the SRE team, your mission is to treat operations as a software problem. You will focus on designing and implementing a next-generation observability and alerting strategy that prioritizes high-cardinality data and meaningful signals over noise. You will spend your time building "self-healing" systems, reducing toil through aggressive automation, and partnering with development teams to bake reliability into the CI/CD pipeline. Your goal is to move us toward a proactive stance where performance bottlenecks are identified and mitigated before they impact the customer. B.S. in Computer Science, Computer Engineering, or a related technical field, or equivalent professional work experience. Understanding of Linux internals and deep networking expertise, including HTTP/2, HTTP/3 (QUIC), and HTTPS/TLS. You should be comfortable debugging protocol-level issues and optimizing traffic flow. Proven ability to automate repetitive tasks and complex workflows using Python or Go Experience configuring and managing modern monitoring suites (e.g., Prometheus, Grafana, ClickHouse) with a focus on creating actionable, high-signal quality alerting. Grasp of Data Structures and Algorithms (DSA) to write efficient, performant code and troubleshoot complex system bottlenecks. Practical knowledge of SLIs, SLOs, Error Budgets, Release Management and Incident Management to drive engineering priorities. Experience managing cloud environments (AWS, GCP, or Azure) using Terraform, Ansible, or Pulumi. Orchestration: Hands-on experience scaling and securing containerized workloads via Kubernetes. A track record of leading "blameless post-mortems" and using those insights to harden the system against future failures. Ability to consult with product teams on service design to improve long-term maintainability. A proactive engineering mindset focused on shifting from "fixing things when they break" to "designing things so they don't break" (or so they fail gracefully). Practical fluency in applying Generative AI tools within SRE and software engineering workflows — from accelerating observability query construction and alert design to building AI-assisted debugging and triage capabilities that encode institutional knowledge into repeatable, context-aware workflows — with the engineering rigour to validate, own, and iterate on AI-assisted outputs in production-adjacent contexts

Requirements

Must-haves

B.S. in Computer Science, Computer Engineering, or a related technical field, or equivalent professional work experience.
Understanding of Linux internals and deep networking expertise, including HTTP/2, HTTP/3 (QUIC), and HTTPS/TLS. You should be comfortable debugging protocol-level issues and optimizing traffic flow.
Proven ability to automate repetitive tasks and complex workflows using Python or Go
Experience configuring and managing modern monitoring suites (e.g., Prometheus, Grafana, ClickHouse) with a focus on creating actionable, high-signal quality alerting.
Grasp of Data Structures and Algorithms (DSA) to write efficient, performant code and troubleshoot complex system bottlenecks.
Practical knowledge of SLIs, SLOs, Error Budgets, Release Management and Incident Management to drive engineering priorities.
Experience managing cloud environments (AWS, GCP, or Azure) using Terraform, Ansible, or Pulumi.
Orchestration: Hands-on experience scaling and securing containerized workloads via Kubernetes.
A track record of leading "blameless post-mortems" and using those insights to harden the system against future failures.
Ability to consult with product teams on service design to improve long-term maintainability.
A proactive engineering mindset focused on shifting from "fixing things when they break" to "designing things so they don't break" (or so they fail gracefully).

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

ci/cdpythongoawsgcpazurekubernetesai

Platform: apple
Effort: medium