Senior Staff Systems Engineer - Performance Engineer
About Nu
Nu is the leading digital bank in Latin America, serving 135 million customers across Brazil, Mexico, and Colombia. The company has been leading an industry transformation by leveraging data and proprietary technology to develop innovative products and services.
Guided by its mission to fight complexity and empower people, Nu caters to customers’ complete financial journey, promoting financial access and advancement with responsible lending and transparency. The company is powered by an efficient and scalable business model that combines low cost to serve with growing returns.
Nu’s impact has been recognized in multiple awards, including Time 100 Most Influential Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks.
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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.
Resource Isolation: Extensive experience with cgroups and the impact of CPU Throttling on JVM quotas within Kubernetes/EKS.
Concurrency: Proven ability to diagnose and resolve complex concurrency problems, including lock contention and race conditions at the instruction level.
Cloud Platforms: Knowledge of AWS infrastructure and its performance characteristics.
Develops and delivers innovative solutions that address team-level or project-level challenges, focusing on medium and long-term impact
Understand the technical aspects, capabilities, and limitations of our systems, contributing to discussions and improvements.
Anticipate technical and product issues, making appropriate design decisions to avoid them
Is enthusiastic about sharing knowledge and mentoring others.
Deep dive into a problem to identify root causes when prioritized.
Benefits
Food/ Meal Card (Vale-Refeição and/or Vale Alimentação)
Chance of earning equity at Nubank
Public Transportation Commuting Benefit (Vale-Transporte)
NuCare - Psychological, Financial and Legal Assistance Program
Life Insurance
Medical Plan
Dental Plan
NuLanguage - Language Course Program
Nucleo - Our learning platform of courses
Extended Parental Leave
Daycare Allowance
Parental Consultancy
Work-from-home Allowance
Gym Partnerships
30 days of paid vacation
Relocation Assistance Package, if applicable
New IDStatusCountryName of the positionLocation for this opportunity (City, Country)
São Paulo
Belo Horizonte
Campinas
Rio de Janeiro
Work Model for this Role
Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/
Our recruitment process may involve the use of artificial intelligence-enabled tools, such as automated interview transcription and analysis, to support the evaluation process. Artificial intelligence is not used to make final hiring decisions; all decisions are made by human reviewers.