Automation & Telemetry Systems Engineer, SPA

Amazon

Reading, Northern (MA, KY)

Hybrid

USD 129,000 - 175,000

Full time

9 days ago

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Job summary

Amazon Robotics in Reading, MA is seeking a System Development Engineer to join the Solution Performance & Automation team within RTS. You will design and implement production monitoring and automation to accelerate detection, standardize responses, and reduce manual work.

The role emphasizes resilience, reuse of automation, and end-to-end service ownership. The team focuses on governed performance signals, Andon escalation, and cross-team integrations, working with field stakeholders to deploy

Qualifications

Bachelor's degree in computer science or equivalent
Experience programming with at least one modern language such as Python, Ruby, Golang, Java, C++, C#, Rust
Experience in automating, deploying, and supporting large-scale infrastructure
Experience with Linux/Unix
Experience with version control systems and CI/CD pipeline implementation
Experience in automation or monitoring frameworks, deployment or development
4+ years of systems design, software development, operations, automation, and process improvement experience
Experience troubleshooting and debugging technical systems

Responsibilities

Build and operate production monitoring services that evaluate robotics telemetry and performance signals for AR solutions, and trigger defined mechanisms (e.g., Andon, escalation, incident workflows) when conditions degrade.
Implement signal evaluation and automation frameworks that are reusable across solutions; default to “build once, standardize, reuse everywhere” and document exceptions with rationale.
Develop closed-loop operational automation including incident creation, routing, enrichment, and escalation integration with partner systems; reduce manual triage and repeated investigative effort.
Improve signal quality and operator trust by reducing false positives/alert noise, tuning mechanisms, and instrumenting measurement for alert precision and operational outcomes.
Build internal tooling that accelerates investigation and diagnosis for field/support stakeholders (explainability views, diagnostics tooling, guided triage workflows), grounded in “mechanisms over dashboards.”
Own the end-to-end lifecycle of your services (design, implementation, testing, deployment, operations). When systems fail, ensure contributing causes are identified and eliminated with permanent fixes, not just mitigations.
Be active in engineering and operational review mechanisms including code reviews, operational readiness reviews (ORRs), correction-of-errors (COEs), and post-incident analyses; use these mechanisms to drive resilience improvements and to coach peers.
Balance constraints and explicitly manage short-term workarounds: avoid them where possible, replace them with long-term solutions, or elevate over-use when it creates systemic risk.
Partner with Data Engineering on pipeline SLAs and reliability. Coordinate with solution/product engineering and field stakeholders through defined interfaces.
Produce clear, accurate, inclusive documentation (technical runbooks, playbooks, operational procedures, and design notes) so systems and artifacts can be maintained and extended by engineers unfamiliar with them.