Principal Software Engineer, Performance Engineering

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Palo Alto, CANew York, NYChicago, ILJersey City, NJ
Employment: Full-time
Posted: 7 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $192k

$124k $246k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $169,550–$214,500.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Principal Software Engineer, Performance Engineering

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As a Principal Software Engineer - Performance Engineering within the Digital Channels & Connectivity organization, you will serve as the senior technical authority for performance engineering across the Payments Technology group. You will own complex cross-stack performance problems ranging from the browser to the operating system, establishing troubleshooting standards and playbooks. Your daily work involves defining NFRs and SLOs, building self-service performance tooling, and designing automated test suites for load, stress, and capacity. You will utilize Java, Spring Boot, Node.js, React, Kubernetes, and Linux performance tools. Key responsibilities include architecting agentic AI-enabled engineering workflows and managing observability via Dynatrace and OpenTelemetry. You will solve high-traffic distributed system challenges, focusing on JVM tuning, queuing theory, and low-latency optimization within a regulated financial services environment.

What does a Software Engineer earn in California?

Median $214500 from 1193 postings across 76 companies.

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Skills

Performance Engineering Java Node.js React Spring Boot Kubernetes EKS Python TypeScript JVM Tuning CI/CD OpenTelemetry Dynatrace Kibana Grafana JMeter k6 Kafka Oracle Istio eBPF Linux Microservices Service Virtualization

What you'll do

Resolve complex cross-stack performance problems from the browser through the back end to the operating system.
Define and institutionalize NFRs and SLOs for latency, throughput, and error budgets across platform releases.
Drive proactive performance engineering through early bottleneck detection, architectural guidance, and capacity modeling.
Build scalable, self-service tooling and infrastructure for performance testing across multiple teams.
Design and maintain automated performance test suites for load, stress, soak, and spike testing.
Embed performance gates into CI/CD pipelines and build dashboards correlating test signals with production telemetry.
Architect and govern agentic AI-enabled engineering workflows to improve delivery speed and code quality.
Set engineering standards and playbooks for troubleshooting and performance sign-offs.

What we're looking for

Extensive performance engineering experience (typically 12+ years) for high-traffic distributed systems.
Deep hands-on expertise debugging distributed systems down to the system call boundary and strong knowledge of JVM internals.
Fluency in Java (back-end) and Node.js/React (front-end) ecosystems, including browser-stack performance debugging.
Hands-on software engineering experience with Java/Spring Boot and Kubernetes (self-managed and EKS).
Strong systems and cloud performance depth, including Linux performance tooling, JVM tuning, containers, and OS-level troubleshooting.
Deep expertise in workload modeling, queuing theory, and statistical analysis of latency and throughput.
Expert with at least one industry-standard load and protocol testing tool (e.g., JMeter, BlazeMeter, k6) with scripting skills in Java, Python, or TypeScript.
Strong observability and APM capabilities using tools such as Dynatrace, OpenTelemetry, Kibana, and Grafana.
Proven ability to influence senior technical leaders, communicate complex trade-offs, and build scalable engineering standards.
Demonstrated experience designing and leading adoption of agentic AI-enabled development practices and responsible AI governance.
Experience operating in financial-services scale, low-latency systems, and/or regulated environments (preferred).
Experience with data-platform performance optimization, service virtualization, and fault injection tools (preferred).
Familiarity with service mesh technologies, advanced performance tooling like eBPF, and practical application of LLMs for test generation (preferred).

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