Senior Lead Software Engineer- Platform / Linux Engineering

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer- Platform / Linux Engineering at JPMorganChase within the Corporate Sector- Infrastructure Platforms, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

As a Senior Platform Engineer within Electronic Trading Services Platforms, you will be a hands-on technical leader responsible for designing, building, and tuning the Linux-based compute and infrastructure platforms that power our low-latency electronic trading businesses. This is a deeply technical, engineering-first role: you will write code and automation, design new systems, architect novel deployment models, and drive latency out of the stack at the hardware, kernel, and network layers. You will also harness modern Agentic AI tooling to accelerate delivery and multiply your impact.

You will work directly with the Electronic Trading Lines of Business to align technical strategy with business goals, solve complex compute and performance-engineering challenges, and mentor partner technical teams. You will serve as an expert, hands-on advisor to peers, leadership, and stakeholders.

Job responsibilities

• Design, build, and operate high-performance Linux platforms for low-latency electronic trading, engineering from the hardware and kernel level up

• Architect new system designs and next-generation deployment models — including immutable infrastructure, containerization, and automated provisioning pipelines

• Perform deep performance engineering and latency optimization: kernel tuning, CPU isolation/pinning, NUMA awareness, IRQ affinity, kernel-bypass networking, and precision time synchronization

• Write production-quality code and automation to build tooling, eliminate manual toil, and improve platform reliability and speed

• Leverage Agentic AI tools and workflows to accelerate engineering tasks, automate research and remediation, and deliver against goals faster

• Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain