Principal Software Engineer - Performance Engineering

JPMorganChase•Palo Alto, CA

About The Position

As a Principal Software Engineer in JPMorganChase’s Commercial and Investment Bank, within the Digital Channels & Connectivity organization and broader Payments Technology group, you will serve as the senior technical authority for performance engineering across platforms. This is a hands-on leadership role focused on solving complex performance problems across the full stack, from browser and back-end systems to the operating system. You will define how performance is engineered, measured, and sustained at scale; establish standards and practices; mentor engineers; and build tools that make high performance a default. You will also shape performance strategy, develop the roadmap, and secure senior stakeholder support for its execution. The role includes building and governing performance testing capabilities and agentic AI-enabled engineering workflows using enterprise-authorized tools within the work environment. You will establish guardrails for validation, security, resiliency, reuse, and responsible AI practices. Employment offers are contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries, as required by Section 19 of the Federal Deposit Insurance Act.

Requirements

Typically 12 or more years of extensive performance engineering experience for high-traffic distributed systems, supported by a strong broader software engineering foundation.
Deep hands-on expertise debugging distributed-system performance problems, including analysis down to the system-call boundary; strong knowledge of JVM internals.
Fluency in Java back-end and Node.js/React front-end ecosystems, including front-end and browser-stack performance debugging practices.
Hands-on software engineering experience with Java/Spring Boot and Kubernetes, including self-managed Kubernetes and EKS.
Strong systems and cloud performance expertise, including Linux performance tooling, JVM tuning, containers, and OS-level troubleshooting under load.
Deep expertise in workload modeling, queuing theory, and statistical analysis of latency and throughput; fluency with percentile-based SLOs and error budgets.
Expertise with at least one industry-standard load and protocol testing tool, such as JMeter, BlazeMeter, or k6, plus scripting and orchestration skills in Java, Python, or TypeScript for performance automation.
Strong observability and APM capabilities using tools such as Dynatrace and/or OpenTelemetry, as well as RUM and synthetic monitoring; experience with dashboarding in Kibana and/or Grafana.
Proven ability to influence senior technical leaders, explain complex performance trade-offs, set engineering standards, and build scalable, self-service performance tooling and practices.
Demonstrated experience designing and leading adoption of agentic AI-enabled development practices across teams, including standards for human-in-the-loop validation, auditability and traceability of changes, and secure handling of sensitive data.
Strong understanding of responsible AI use and control expectations in engineering workflows, including security and resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.

Nice To Haves

Experience operating in financial-services-scale or low-latency systems and/or regulated environments.
Experience with data-platform performance optimization, such as Oracle tuning, JDBC pool tuning, Kafka throughput and partitioning, or caching strategies.
Experience with service virtualization and fault-injection tools such as WireMock, Mountebank, or Toxiproxy, including record-replay and dynamic templating.
Familiarity with modern cloud-native load-testing frameworks such as k6.
Familiarity with service-mesh technologies such as Istio or Linkerd and traffic-control patterns such as rate limiting and backpressure.
Knowledge of advanced performance tools such as perf and eBPF, and production-grade troubleshooting practices.
Practical application of LLMs for test generation, anomaly detection, or automated reporting in engineering workflows.

Responsibilities

Own complex cross-stack performance problems, from the browser through the back end to the operating system, and establish troubleshooting standards and playbooks.
Define and institutionalize non-functional requirements and service-level objectives, including p95/p99 latency, throughput, ramp profiles, and error budgets; provide final technical performance sign-offs for platform releases.
Drive proactive performance engineering through early bottleneck detection, architectural guidance, capacity modeling, and runtime tuning.
Build tooling and infrastructure that make performance testing a scalable, self-service capability across teams.
Design and maintain automated load, stress, soak, spike, and capacity test suites with environment-aware, fully automated execution.
Embed performance gates in CI/CD pipelines and build dashboards that correlate test signals with production telemetry against defined SLOs.
Architect and govern agentic AI-enabled engineering workflows using enterprise-authorized tools to improve delivery speed, code quality, and operational outcomes, including PR review assistance, test generation and maintenance, release-readiness checks, incident triage, and root-cause acceleration.
Define guardrails for AI-enabled engineering workflows, including validation, security, resiliency, and reuse across teams.
Apply SDLC toolchain knowledge, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Influence senior technical leaders, communicate performance trade-offs, set engineering standards, mentor engineers, and build practices that enable high performance across teams.