What you'll do
As a Software Engineer on our Site Reliability team at Sierra, you will be responsible for defining and building the foundation of reliability, observability, and scalability across Sierra’s AI-driven infrastructure. You’ll partner closely with our core engineering and product teams to ensure our systems are highly available, efficient, and built for growth.
Own Sierra’s observability stack—monitoring, alerting, logging, and tracing—to give engineers clear visibility into system health and performance.
Partner with product and platform engineers to design systems that are reliable and scalable from day one—not as an afterthought.
Design and implement scalable, reliable, and secure cloud infrastructure (AWS) using Terraform and modern DevOps tooling.
Improve the reliability and scalability of our LLM deployments, ensuring robust, performant, and cost-effective operation.
Lead improvements to deployment pipelines, CI/CD tooling, and incident management processes to reduce downtime and response time.
Define the foundation of SRE practices at Sierra, influencing culture, tooling, and best practices across the engineering org.
What you'll bring
5+ years of hands-on experience in Site Reliability or Infrastructure engineering roles for complex SaaS or cloud-based systems.
Experience designing for availability, scalability, and reliability at both infrastructure and application layers.
Deep experience with Terraform, AWS services, container orchestration, and cloud networking (including IAM and VPC architecture).
Strong background in observability systems (e.g., Prometheus, Grafana, Datadog, or similar).
Experience working with enterprise customers and familiarity with their compliance and networking needs along with integration patterns.
Comfortable working in fast-moving environments and collaborating across product, ML, and core engineering teams.
Degree in Computer Science or a related field, or equivalent professional experience.
Even better...
Experience with LLM infrastructure — optimizing inference performance, managing fine-tuned models, or large-scale model deployment.
Past experience in an early-stage startup environment, especially defining SRE culture and tooling from scratch.
Familiarity with incident management automation or self-healing infrastructure patterns.