Staff Site Reliability Engineer
- Compensation
- Not disclosed
- Location
- Remote (Ann Arbor, Michigan, US)
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Sre
- Posted
Skills
Must have
- Demonstrated experience designing, building, or operating agentic AI/LLM-based systems in production, held to the same quality-first, test-driven rigor as traditional infrastructure code, not just pro
- Embody a quality-first and security-first culture in all that you do
- 10+ years of experience with Kubernetes/Docker in at least one top-tier cloud provider (Azure, GCP, AWS), including production-scale multi-tenant or multi-cluster environments
- 10+ years coding experience (Python, Go, Java, or similar) with a track record of building tools/platforms used by other engineers, not just scripts
- 10+ years with IaC and CI/CD tooling (Terraform/OpenTofu, FluxCD or similar GitOps tooling, Jenkins/GitHub Actions)
- Strong, provable Linux and networking fundamentals (TCP/IP and application-layer)
- Practical experience integrating or operating LLM/agentic AI systems in a production context this can be API-based orchestration, LLM gateways, or agent frameworks
- A track record of authoring technical documentation (design docs, ADRs, runbooks) that other engineers actually use
- Demonstrated mentorship of other engineers, without needing formal management authority to do it
- Strong bias for action over endless planning, hands-on, has made mistakes, learned from them, and can weigh risk vs. customer impact under pressure
- Clear, empathetic communicator, comfortable pushing back on architecture decisions across teams
- Operational experience with monitoring/alerting systems (Prometheus, Grafana, Loki, Sentry, Signoz or equivalents)
Nice to have
- Experience with elements of our current tech stack are a plus: Kubernetes, FluxCD, Terraform, Helm Charts, Prometheus, Elasticsearch, Python, Java, Kafka, Postgres, and Jenkins
- Previous experience or a keen interest in industrial IoT, analytics, or manufacturing a plus
Full description
About the team
Sight Machine is built on the shoulders of a unique, robust and highly scalable Infrastructure as Code model. This enables the creation and operation of customer instances in our ecosystem in a standardized and simplified manner. We are looking for team members to help us build, maintain, and improve the infrastructure that makes Sight Machine the leading provider of Manufacturing Data Pipelines and Analytics.
Great things happen when people can bring their authentic selves to work. We empower all of our team members to share their perspectives, passions and experiences because collectively we make a better, stronger team through always “open communications” mind.
Our team collaborates closely with peers & cross functional stakeholders throughout the business, our clients on the forefront of digital transformation, and the cutting edge of digital manufacturing thought leadership.
Sight Machine has offices in San Francisco, CA and Ann Arbor, Mi. We do have a remote-friendly culture with people based all around the US and the rest of the world. For this role in particular, the ideal candidate is located near either of our offices and willing to work in a hybrid capacity. We would still consider 100% remote for exceptional candidates if they aren’t located near an office.
About the role
Join the Cloud Infrastructure Team as a technical leader driving reliability, automation, and scalability across the systems running Sight Machine's platform. You'll operate at the intersection of classic SRE discipline which include IaC, CI/CD, observability, incident response and the emerging demands of running agentic AI systems in production: LLM gateways, agent orchestration, and the operational patterns that come with non-deterministic workloads.
This is a senior level IC role. You'll help set and drive technical direction for infrastructure and reliability practices across teams, mentor senior engineers, and be a primary escalation point for the org's hardest systems problems while still being hands-on with code, infrastructure, and incidents.
Success requires deep technical range, sound judgment on risk vs. customer impact, and the ability to influence architecture decisions across Development Engineering without formal authority.
What You’ll Actually Work On
- Champion an agentic-AI-first engineering mindset: identify where AI-driven automation and agent-based tooling can replace manual toil, and hold that work to the same quality, testing, and reliability bar as any other production system
- Evolve reliability practices for meeting reliability SLO’s, error budgets, drive incident postmortems to systemic (not just symptomatic) fixes, and lead reliability reviews for new services before they hit production