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Sight Machine

SaaS manufacturing analytics platform with AI

Staff Site Reliability Engineer

Site Reliability EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Demonstrated experience designing, building, or operating production agentic AI or large language model systems with quality-first, test-driven rigor.
  • At least 10 years of experience with Kubernetes and Docker in a top-tier cloud provider, including production-scale multi-tenant or multi-cluster environments.
  • At least 10 years of coding experience with Python, Go, Java, or a similar language, including building tools or platforms used by other engineers.
  • At least 10 years of experience with infrastructure as code and continuous integration/continuous delivery tooling, such as Terraform/OpenTofu, FluxCD or similar GitOps tooling, and Jenkins/GitHub Actions.
  • Strong Linux and networking fundamentals, including TCP/IP and application-layer networking.
  • Practical production experience integrating or operating LLM or agentic AI systems, such as API-based orchestration, LLM gateways, or agent frameworks.
  • Experience authoring technical documentation, including design documents, architecture decision records, and runbooks, used by other engineers.
  • Demonstrated ability to mentor engineers without formal management authority.
  • Operational experience with monitoring and alerting systems such as Prometheus, Grafana, Loki, Sentry, or Signoz.
  • Deep understanding of cloud performance and ability to diagnose and resolve bottlenecks.
  • Ability to weigh risk against customer impact under pressure and communicate clearly across teams.
  • Adherence to quality-first and security-first practices.

Responsibilities

  • Identify opportunities for AI-driven automation and agent-based tooling to replace manual toil, ensuring the work meets production quality, testing, and reliability standards.
  • Evolve reliability practices for meeting reliability service-level objectives and managing error budgets; drive incident postmortems toward systemic fixes and lead reliability reviews for new services before production.
  • Troubleshoot and resolve complex cross-layer systems problems involving continuous integration and delivery, container orchestration, networking, operating systems, cloud resources, databases, and agentic AI or large language model orchestration layers.
  • Design, build, and operate infrastructure for agentic AI workloads, large language model gateway routing, agent orchestration frameworks, monitoring of non-deterministic or AI-driven services, and operational tooling to run them reliably at scale.
  • Architect and instrument monitoring, alerting, and observability infrastructure for critical services, including AI-driven systems.
  • Author and continuously improve operational runbooks and automation, incorporating agentic or AI-assisted tooling such as automated triage and AI-assisted incident response when it measurably reduces toil.
  • Design and build internal platforms and developer tooling for other engineers.
  • Participate in on-call coverage and help evolve the program, including escalation paths and reducing avoidable pages through automation.
  • Stay engaged with operational issues, customer problems, and team needs, including outside formal tickets or rotations.
  • Mentor senior and mid-level engineers and act as a technical sounding board across teams.
  • Proactively identify and drive self-directed cross-team initiatives to improve stability, reliability, and availability.

Desired Qualifications

  • Experience with elements of the current technology stack, including Kubernetes, FluxCD, Terraform, Helm charts, Prometheus, Elasticsearch, Python, Java, Kafka, Postgres, and Jenkins.
  • Previous experience or a keen interest in industrial Internet of Things, analytics, or manufacturing.

About the Company

Sight Machine

SaaS manufacturing analytics platform with AI

Sight Machine provides an AI-powered software platform for manufacturing analytics. It collects data from factories via connected equipment and systems, then uses AI to analyze the data in real time and deliver actionable insights through dashboards and reports. This helps large manufacturers improve operational efficiency, reduce waste, and enhance product quality. The platform is offered as a software-as-a-service (SaaS) with optional custom analytics and consulting services. Compared to competitors, Sight Machine focuses on enterprise-scale, real-time analytics across multiple industries (such as automotive, electronics, and consumer goods) to give companies a unified view of their operations and enable data-driven decisions at scale. The goal is to help manufacturers optimize production, predict maintenance needs, and lower costs to increase profitability.

Company size
51-200
Company stage
Late Stage VC
Total funding
$69.9M
Headquarters
San Francisco, CA
Founded
2012

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