Senior Garbage Collection Tuning Specialist

HL Mando Corporation do Brasil; Abu Dhabi; Remote; AED 300,000 - 540,000; Full time

Benefits offered by this job

100% fully remote work arrangement

Job summary

HL Mando Corporation do Brasil is seeking a Senior Garbage Collection Tuning Specialist to optimize memory management and reduce application pauses in memory‑intensive systems. The role is fully remote with 100% work-from-home flexibility, collaborating with software and platform teams to deliver measurable performance improvements.

The ideal candidate has deep JVM and .NET GC knowledge, hands-on profiling experience with JFR/JMC/VisualVM, and a proven track record tuning GC configurations

Qualifications

• Hands-on experience diagnosing and tuning garbage collection behavior in production applications.
• Strong understanding of GC algorithms, memory allocation, and heap management.
• Familiarity with Docker, Kubernetes, and cloud-hosted workloads.

Responsibilities

• Analyze GC behavior across environments to identify performance bottlenecks and long pause times.
• Design and tune GC configurations based on workload characteristics and resources.
• Investigate heap utilization, allocation rates, and memory retention patterns.
• Benchmark GC pauses and memory usage before and after tuning changes.
• Collaborate with developers to optimize code paths that affect memory usage.

Skills

Java

JVM optimization

Memory management

Performance profiling

GC tuning

Diagnostics tools

Education

Bachelor's degree in Computer Science

Master's degree (advantage)

Tools

Java Flight Recorder

Java Mission Control

VisualVM

Eclipse Memory Analyzer

async-profiler

Job description

Job Title: Senior Garbage Collection Tuning Specialist

Location: UAE (Remote)

Employment Type: Full-Time

Level: Senior

Work Arrangement: Fully Remote — 100% Work From Home

About Us

We are committed to improving software performance, runtime stability, and memory efficiency through advanced garbage collection analysis and optimization. We are seeking an experienced Senior Garbage Collection Tuning Specialist to evaluate memory management behavior, optimize garbage collection configurations, reduce application pauses, and improve the performance and reliability of memory-intensive software systems.

The successful candidate will have strong expertise in runtime memory management, garbage collection algorithms, heap analysis, performance profiling, and application diagnostics. This role will collaborate with software engineering, platform engineering, DevOps, and architecture teams to resolve complex memory-related performance issues and establish effective runtime optimization practices.

Key Responsibilities

• Analyze garbage collection behavior across production and non-production environments to identify performance bottlenecks, excessive collection activity, long pause times, and memory utilization issues.
• Design, implement, and optimize garbage collection configurations based on application workload characteristics, latency requirements, throughput objectives, and available system resources.
• Investigate heap utilization, object allocation rates, memory retention patterns, allocation pressure, and garbage collection frequency.
• Analyze stop-the-world pauses, concurrent collection activity, heap fragmentation, promotion failures, and other memory management challenges.
• Evaluate and tune garbage collectors across relevant runtime environments, including Java Virtual Machine (JVM) collectors such as G1 GC, ZGC, Shenandoah, and Parallel GC, where applicable.
• Assess garbage collection behavior in .NET environments and other managed runtimes when required by the organization's technology stack.
• Collect and interpret garbage collection logs, heap dumps, thread dumps, runtime metrics, allocation profiles, and performance traces.
• Identify memory leaks, excessive object creation, inefficient object lifecycles, unintended object retention, and inefficient data structures.
• Work with application developers to optimize code patterns that contribute to unnecessary allocations, excessive garbage generation, or inefficient memory usage.
• Establish appropriate heap sizing, young and old generation configurations where applicable, allocation thresholds, and other runtime parameters.
• Evaluate the relationship between heap size, container memory limits, operating system resources, and garbage collection performance.
• Diagnose memory pressure and out-of-memory errors in virtual machines, containers, cloud environments, and distributed application deployments.
• Conduct controlled performance experiments to compare garbage collection algorithms, runtime configurations, and memory optimization strategies.
• Benchmark application throughput, latency percentiles, pause duration, allocation rates, and memory consumption before and after tuning changes.
• Optimize garbage collection behavior for latency-sensitive applications, high-throughput services, large-scale transaction processing systems, and memory-intensive workloads.
• Investigate the causes of full garbage collections, excessive promotion, allocation stalls, concurrent collection failures, and unexpected runtime pauses.
• Collaborate with platform and infrastructure teams to ensure runtime configurations align with container orchestration, autoscaling, and resource allocation policies.
• Integrate garbage collection metrics and alerts into application performance monitoring, observability, and incident management systems.
• Develop automated scripts and diagnostic utilities for log analysis, heap trend monitoring, performance regression detection, and configuration validation.
• Establish performance baselines and recommended garbage collection tuning standards for different application profiles.
• Support production incident investigations involving memory exhaustion, excessive pause times, degraded throughput, and unstable application behavior.
• Prepare technical reports documenting root causes, tuning recommendations, experimental results, and measurable performance improvements.
• Review runtime upgrades and software changes to assess their impact on garbage collection behavior and memory management.
• Develop internal guidelines and provide technical mentoring on memory profiling, garbage collection fundamentals, and runtime optimization practices.
• Maintain documentation of approved runtime settings, tuning procedures, diagnostic workflows, and operational best practices.
• Perform all responsibilities remotely using secure development environments, profiling tools, monitoring systems, and digital collaboration platforms.

Qualifications and Experience

• Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Information Technology, or a related technical discipline.
• A master's degree in Computer Science, Systems Engineering, or a related field is an advantage.
• At least 5–8 years of experience in software engineering, runtime performance engineering, JVM optimization, memory management, or application diagnostics.
• Demonstrated hands-on experience diagnosing and tuning garbage collection behavior in production applications.
• Strong understanding of garbage collection algorithms, object lifecycles, memory allocation, heap management, reference handling, and memory reclamation.
• Extensive knowledge of Java Virtual Machine internals, including heap regions, generations where applicable, allocation paths, safepoints, and garbage collection logging.
• Practical experience tuning relevant JVM collectors, such as G1 GC, ZGC, Shenandoah, or Parallel GC, according to workload and runtime version requirements.
• Familiarity with .NET garbage collection and managed memory concepts is an advantage.
• Strong understanding of memory leaks, object retention, heap fragmentation, allocation pressure, and out-of-memory conditions.
• Experience analyzing garbage collection logs, heap dumps, thread dumps, allocation profiles, and runtime performance metrics.
• Proficiency with diagnostic and profiling tools such as Java Flight Recorder (JFR), Java Mission Control (JMC), VisualVM, Eclipse Memory Analyzer (MAT), async-profiler, or comparable tools.
• Experience with performance benchmarking, load testing, stress testing, and latency analysis.
• Knowledge of operating system memory behavior, process resource limits, virtual memory, CPU scheduling, and container resource constraints.
• Familiarity with Docker, Kubernetes, cloud-hosted workloads, and containerized Java or other managed-runtime applications.
• Understanding of application performance monitoring, distributed tracing, centralized logging, and runtime observability.
• Strong programming skills in Java or another relevant managed-runtime language, with the ability to review and improve memory-intensive code.
• Ability to distinguish garbage collection problems from application design issues, memory leaks, database bottlenecks, and infrastructure constraints.
• Strong analytical and troubleshooting skills, including the ability to develop reproducible experiments and validate optimization results.
• Excellent written and verbal communication skills, with the ability to explain complex runtime behavior to engineering and operations teams.
• Experience documenting technical investigations and communicating evidence-based tuning recommendations.
• Ability to work independently, manage multiple performance investigations, and collaborate across distributed teams.
• High standards of technical accuracy, operational reliability, confidentiality, and engineering quality.

Key Performance Indicators (KPIs)

• Garbage Collection Pause Reduction: Reduce excessive pause durations and improve latency consistency in accordance with application requirements.
• Memory Efficiency: Improve heap utilization and reduce unnecessary allocation and memory retention.
• Application Throughput: Increase processing capacity while maintaining acceptable latency and system stability.
• Runtime Stability: Reduce memory-related incidents, out-of-memory errors, and unexpected full garbage collections.
• Performance Optimization: Deliver measurable improvements against established memory and runtime performance baselines.
• Diagnostic Effectiveness: Identify root causes of garbage collection and memory management issues accurately and efficiently.
• Configuration Quality: Maintain appropriate, tested, and documented runtime settings for supported workloads.
• Production Reliability: Minimize performance degradation associated with memory pressure and garbage collection behavior.
• Monitoring Coverage: Improve visibility into garbage collection metrics, memory trends, and runtime health indicators.
• Regression Prevention: Establish repeatable benchmarking and monitoring practices to detect performance regressions early.
• Documentation Quality: Maintain comprehensive records of investigations, tuning changes, and performance outcomes.
• Engineering Collaboration: Help development and platform teams adopt effective memory management and garbage collection practices.

What We Offer

• Competitive compensation based on qualifications, experience, and technical expertise.
• 100% fully remote work arrangement, with no routine office attendance required.
• Opportunities to solve complex memory management and runtime performance challenges.
• Exposure to modern runtime technologies, advanced profiling tools, and large-scale application environments.
• A collaborative engineering culture focused on technical excellence, reliability, and continuous improvement.
• Opportunities for professional development and career advancement in runtime performance engineering.