Senior Site Reliability Engineer

No H1 or C2C. Must be Permanent Resident or US Citizen

Senior Site Reliability Engineer

Description and Requirements

About Our Team

We are building
Quantum
, a next‑generation hybrid AI platform that spans Windows, Android, and cloud. As part of this vision, we are expanding the reliability engineering organization that powers cross‑device Personal AI.

We are looking for
Senior Site Reliability Engineers (SREs)
to help us build and evolve the foundational reliability, observability, and operations capabilities that ensure fast, safe, and dependable for millions of users.

This role may support one of several teams within the SRE organization (e.g., Observability, Operations, or Service Reliability), depending on your strengths and interests.

Operating with the
speed, ownership, and creative latitude of a startup
—yet supported by the scale, resources, and technical depth. We are building new systems, new tooling, and new operational models from the ground up, and we are doing so with clarity, intention, and high engineering standards.

Location:
Open to remote work in the US. The preferred work location is Chicago, IL.

What You Might Work On

As a Senior SRE, you may be responsible for a subset of the following, depending on team placement and skill alignment:

Reliability & Performance Engineering

- Improving the availability, scalability, and performance of distributed systems across device, edge, and cloud.
- Defining or refining
SLIs, SLOs, and error budgets
for critical services.
- Leading initiatives to remove single points of failure, improve resilience, and reduce operational risk.

Operational Excellence

• Participating in on‑call rotations and contributing to incident response, triage, and post-incident reviews.
• Developing automation, runbooks, and self‑healing systems to reduce alert noise and MTTR.
• Enhancing operational readiness and supporting incident prevention programs.

Observability & Insight

- Designing or improving observability systems using
OpenTelemetry
,
Grafana
, and modern signal pipelines.
- Building dashboards, analytics, and alerting that illuminate system health and AI service behavior.
- Ensuring telemetry is reliable, actionable, and tied to real‑world outcomes.

Deployments & Change Safety

• Improving reliability of CI/CD workflows, including phased rollouts, canaries, shadow testing, and safe rollback mechanisms.
• Contributing to the evolution of deployment tooling for device+edge+cloud hybrid systems.

Systems Design & Collaboration

• Influencing architectural decisions by injecting reliability, observability, and operational considerations early in design.
• Collaborating with AI/ML engineers, platform engineers, firmware teams, and product partners to deliver robust, dependable user experiences.

Basic Qualifications

- 10+ years
of experience in
Site Reliability Engineering, Production Engineering, DevOps, or large‑scale distributed systems operations
- Bachelor’s Degree in Computer Science, Engineering, or a related technical discipline
- Strong experience running
production distributed systems
at scale
- Proficiency in at least one modern programming language (e.g., Python, Go, Java, C++)
- Strong understanding of
Linux systems
, networking fundamentals, and system performance tuning
- Experience with monitoring/observability (metrics, logs, tracing)
- Hands‑on experience with cloud environments (Azure, AWS, or GCP)
- Experience in incident management, on‑call rotations, and postmortem processes

Preferred Qualifications

- Deep experience with
Azure
cloud services
- Experience with
OpenTelemetry
for end‑to‑end instrumentation
- Strong familiarity with
Grafana
, Prometheus, Loki, Tempo, or similar tools
- Experience supporting
AI/ML systems
, model serving, or data‑intensive workloads
- Background with hybrid architectures (device + edge + cloud)
- Experience improving deployment reliability and progressive delivery systems
- Passion for automation, reliability engineering, and reducing operational friction

What Success Looks Like

• Systems become more observable, reliable, and predictable.
• Incidents are resolved quickly, and follow‑up improvements prevent recurrence.
• Alerting becomes more accurate, actionable, and trusted.
• Deployments become safer and more consistent.
• Teams move faster because reliability foundations are strong and intuitive.