Principal Software Engineer - Performance Engineering

Principal Software Engineer - Performance Engineering

Palo Alto, CA, United States and 3 more

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Job Information

Job Identification

210799011

Job Category

Software Engineering

Business Unit

Commercial & Investment Bank

Posting Date

10/20/2026, 09:13 AM

Locations

3223 Hanover St, Palo Alto, CA, 94304, US

Job Schedule

Full time

Base Pay/Salary

Palo Alto,CA $204,250.00-$285,000.00; New York,NY $204,250.00-$285,000.00; Chicago,IL $175,750.00-$260,000.00; Jersey City,NJ $204,250.00-$285,000.00

Job Description

If you are looking for a game-changing career, working for one of the world's leading financial institutions, you've come to the right place.

As a Principal Software Engineer at JPMorganChase within the Commercial and Investment Bank's Digital Channels & Connectivity organization, part of our broader Payments Technology group, you will be the senior technical authority for performance engineering across our platforms. This is a hands-on leadership role for an engineer who sets direction rather than waits for it.

You will tackle the performance problems that others cannot, work across the full stack from the browser to the back end and down to the operating system, and define how performance is engineered, measured, and sustained at scale. You will set standards, mentor strong engineers, and build the tools and practices that make high performance a default rather than an afterthought. Just as importantly, you will shape the strategy, build the roadmap, and earn the support of senior stakeholders to see it through.

If you want real ownership, deep technical challenges, and the chance to raise the bar for an entire organization, this is the role for you.

Job responsibilities

Own the hardest cross-stack performance problems—from the browser through the back end down to the operating system—and establish the troubleshooting standards and playbooks others follow

Define and institutionalize NFRs and SLOs (p95/p99 latency, throughput, ramp profiles, error budgets) and serve as the final technical authority for performance sign-offs across platform releases

Drive proactive performance engineering through early bottleneck detection, architectural guidance, capacity modeling, and runtime tuning

Build the tooling and infrastructure that turns performance testing into a scalable, self-service capability across teams

Design and maintain automated performance test suites (load, stress, soak, spike, capacity) with environment-aware, fully automated execution

Embed performance gates into CI/CD pipelines and build dashboards that correlate test signals with production telemetry against defined SLOs

Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams

Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale

Required qualifications, capabilities, and skills

Extensive performance engineering experience (typically 12+ years) for high-traffic distributed systems—web, APIs, microservices, event-driven, and data-centric—built on a strong broader software engineering foundation

Deep hands-on expertise debugging performance problems in distributed systems, with the ability to go as deep as the system call boundary; strong knowledge of JVM internals

Fluency in both the Java (back-end) and Node.js/React (front-end) ecosystems, including front-end and browser-stack performance debugging best practices

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 under load

Deep expertise in workload modeling, queuing theory, and statistical analysis of latency and throughput; fluent with percentile-based SLOs and error budgets

Expert with at least one industry-standard load and protocol testing tool (e.g., JMeter, BlazeMeter, k6), with 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, plus RUM and synthetic monitoring, with dashboarding in Kibana and/or Grafana

Proven ability to influence senior technical leaders, communicate complex performance trade-offs clearly, set engineering standards, and build tooling and practices that make high performance a scalable, self-service capability across teams

Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data

Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse

Preferred qualifications, capabilities, and skills

Experience operating in financial-services scale, low-latency systems, and/or regulated environments

Experience with data-platform performance optimization (e.g., Oracle tuning, JDBC pool tuning, Kafka throughput/partitioning, caching strategies)

Experience with service virtualization and fault injection tools (e.g., WireMock, Mountebank, Toxiproxy), including record-replay and dynamic templating

Familiarity with modern cloud-native load testing frameworks such as k6

Familiarity with service mesh technologies (Istio/Linkerd) and traffic-control patterns (rate limiting, backpressure)

Knowledge of advanced performance tooling (e.g., perf, eBPF) and production-grade troubleshooting practices

Practical application of LLMs for test generation, anomaly detection, or automated reporting in engineering workflows

This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.