Senior Staff Systems Engineer - Performance Engineer

About Nu

Nu serves more than 140 million customers, guided by a mission to fight complexity and empower people. The company has been leading an industry transformation through innovative products and human-centered services.

Proprietary technology and data at scale power Nu’s digital platform, built to promote financial access, advancement, and transparency. Its business model thrives on customer love and lower costs, feeding a flywheel of growth and profitability. Visit our Institutional Page

About the Role

The Systems Performance team is part of the Computing Squad consists of two distinct workstreams —Orchestration and System Performance —each with its own approach and challenges on managing and improving the foundational infrastructure where the majority of the Nubank's workloads runs.

The Performance team is focused on building deep diagnostic tools and performing high-level analysis to reduce latency,  infrastructure costs and increase services efficiency.

You will be responsible for leading complex performance investigations, identifying systemic bottlenecks, and driving efficiency across one of the largest JVM-based microservice architectures in the world.

Our core principles and behaviors include ownership, simplicity, veracity-first, teamwork, and a focus on quality over quantity. During a normal work day, you will interact with critical infrastructure layers, from the Linux Kernel and JVM internals to cloud-wide orchestration.

You'll be responsible for

Leading Deep-Dive Investigations: Conduct high-level performance analysis to identify and resolve systemic bottlenecks across our global JVM-based microservices architecture.

Optimizing Resource Efficiency: Drive initiatives to reduce infrastructure costs and latency by fine-tuning JVM parameters, Garbage Collection (ZGC, G1), and memory management (heap and off-heap).

Building Diagnostic Tooling: Develop and implement advanced observability tools using eBPF, JFR, and Flamegraphs to provide real-time insights into kernel and runtime behavior.

Kernel & Runtime Alignment: Bridge the gap between the Linux Kernel and the JVM, optimizing thread scheduling (CFS/EEVDF) and managing resource isolation (cgroups/throttling) within our Kubernetes environment.

Architecting Scalable Solutions: Design and deliver innovative infrastructure improvements that address long-term performance challenges, ensuring our systems scale ahead of demand.

Technical Mentorship & Culture: Share expertise on JVM internals and performance best practices with the wider Engineering team, fostering a culture of technical excellence and "quality over quantity."

Root Cause Excellence: Deep dive into complex concurrency issues, lock contentions, and memory leaks, providing definitive fixes for high-impact technical debt.

Strategic Collaboration: Work closely with the Computing Squad to align orchestration strategies with system performance goals, ensuring a seamless interface between infrastructure and workloads.

We are looking for a person who has

Expertise in JVM Internals: Deep, low-level knowledge of the JVM is essential. You must understand how the JVM works "under the hood," including JIT compilation (C1/C2), class loading, and intrinsic methods.

JVM Tuning & Garbage Collection: Extensive experience with GC algorithms (ZGC, G1, Shenandoah), including the ability to tune them for massive heaps and ultra-low latency requirements.

OpenJDK Contribution (Major Plus): Previous experience contributing to the OpenJDK project or other low-level runtime environments is a significant advantage.

Linux Kernel & Scheduling: Deep understanding of the Linux Scheduler (CFS/EEVDF), thread scheduling, and how the kernel manages high-concurrency Java workloads.

Memory Architecture: Mastery of heap and off-heap memory management, including Direct Buffers, memory-mapped files, and diagnosing complex memory leaks.

Advanced Diagnostics: Mastery of diagnostic tools such as Flamegraphs, JFR (Java Flight Recorder), eBPF, and performing large-scale heap dump analysis.