Principal Engineer - Distributed Systems $160,000 - $200,000 Posted 2 hours ago

Fuel Talent LLC; Seattle, Northern (WA, KY); Hybrid; USD 160,000 - 200,000; Full time

Job summary

Fuel Talent LLC seeks a Principal Engineer for distributed systems in Seattle, WA. This role is hybrid, requiring 2–4 days onsite, and offers a base salary of $160,000–$200,000 with up to 1% equity.

You will own and operate a major vertical of a high-scale infrastructure platform handling AI workloads and large data sets. You will design systems that stay fair, observable, and predictable under contention, and you will write production code, troubleshoot performance issues, and drive operational

Qualifications

• Experience building and operating high-volume, low-latency services on shared infrastructure.
• Understanding of multi-tenant workloads and fair resource sharing.
• Familiarity with end-to-end production ownership: code, deployment, observability, and incident response.

Responsibilities

• Own, operate, and improve major vertical of a high-scale distributed infrastructure platform.
• Write production code and investigate performance and reliability issues.
• Operate what you build and establish patterns for others to follow.
• Ensure isolation and fair admission for large customer workloads across services.

Skills

Distributed systems

Workload isolation

Admission control

Tail-latency

Reliability

Large data stores

Deployment automation

Performance optimization

AI infrastructure

Job description

# Principal Engineer - Distributed SystemsApplyApplyPosted about 3 hours agoShareFull TimeSeattle, Washington$160,000 - $200,000 Annually## Principal Engineer — Distributed Systems**Location:** Seattle, WA — Hybrid, 2–4 days/week onsite **Compensation:** $160,000–$200,000 base + up to 1% equity## What is the core job?Own, operate, and improve a major vertical of a high-scale distributed infrastructure platform.The platform runs large-scale services that execute significant query volumes, process substantial AI workloads, and ingest, transform, store, and retrieve large volumes of structured and unstructured data.The difficult part is not simply adding capacity. Workloads vary enormously, with some customer workloads being orders of magnitude larger or more demanding than others while running on shared infrastructure. You will design systems that remain fair, isolated, observable, and predictable under contention.This is an end-to-end ownership role. There is no separate platform, SRE, infrastructure, or database team responsible for finishing the work. When you design a system, you will also own its infrastructure definitions, deployment configuration, production promotion, observability, operational behavior, and incident response.This is not primarily an architecture or advisory position. You will write production code, investigate performance and reliability problems, operate what you build, and establish technical patterns that other engineers can use.## Success means* One customer’s workload cannot degrade another’s. Large jobs are isolated, admission is fair, and tail latency remains predictable under contention.* Critical services have clear ownership, strong observability, understood failure modes, and reliable recovery paths.* The system handles extreme variance in workload size, query patterns, ingestion volume, and processing demand without requiring manual intervention.* Bottlenecks across ingestion, storage, retrieval, orchestration, and AI-processing pipelines are identified and removed.* Infrastructure, application code, deployment configuration, and production operation are treated as one engineering responsibility rather than separate functions.* The engineering team makes better architectural decisions because you contribute both technical leadership and working implementations.## Your backgroundYou have built and operated high-volume, low-latency services on shared infrastructure.### Your experience may include:* Distributed systems* Workload isolation and multi-tenancy* Admission control, queuing, scheduling, and backpressure* Tail-latency management* Reliability and failure recovery* Large relational and NoSQL data stores* Production infrastructure and deployment automation* High-scale data processing or AI infrastructure* Performance optimization and capacity management## ApplyDrop files to attach,orbrowseBrowse to attach file for Resume Upload. Allowed file types: .pdf,.docx,.doc