Software Engineer, Performance Insights

About the Role

We are seeking a highly skilled engineer to lead the design and development of systems that unlock deep system performance insights at scale. This role involves building innovative solutions-from conceptualization through production-that leverage statistical analysis and machine learning to diagnose performance issues, predict regressions, and optimize resource utilization across complex infrastructures. Your focus will be on developing broadly applicable, robust systems for real-world performance monitoring, analysis and optimization. Real world production system examples in this domain include FBDetect(Meta) , Servicelab(Meta) , FleetIO(Google) , and FP-Growth(Meta)

Key Responsibilities

• System Architecture & Design:
• • Architect and build end-to-end performance monitoring systems that detect and analyze minute regressions and performance anomalies in production environments.
  • Design scalable solutions that collect, process, and analyze large volumes of performance data from diverse environments (e.g., bare metal, VMs, cloud infrastructures).
  • Develop modular systems that integrate statistical techniques and machine learning models to extract actionable insights and drive continuous performance improvements.
• Statistical Analysis & Machine Learning:
• • Apply advanced statistical methods (e.g., change point detection, trend analysis) to identify subtle performance variations and anomalies in noisy datasets.
  • Integrate machine learning techniques, including reinforcement learning and predictive analytics, to optimize resource allocation and proactively detect performance degradations.
  • Collaborate with data science teams to refine models and validate findings against production data.
• Software Development:
• • Write clean, efficient code in languages such as C/C , Go, or Python, ensuring high performance and low overhead in critical production systems..
• Collaboration & Communication:
• • Work cross-functionally with infrastructure, product, and operations teams to integrate performance insights into broader system optimization strategies.
  • Present data-driven insights and performance recommendations to technical and non-technical stakeholders.
  • Mentor junior team members and contribute to best practices in performance engineering and analytics.

Basic Qualifications

• Bachelor's or higher degree in one of Computer Science, Data Science, AI/ML, Statistics, Mathematics or a related technical field.
• Strong grasp of statistical analysis and machine learning techniques and willingness to apply them to the system performance domain.
• 2 years of experience in building production-grade Data/ML systems.
• Proficiency in one or more programming languages (e.g., C/C , Go, Python)

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Statistics, Data Science or related fields
• 5 years of experience in AI/Data Science
• Experience designing and deploying in-production systems for performance regression detection or optimization.
• Background in implementing automated root cause analysis, anomaly detection, or predictive modeling using ML frameworks.
• Understanding of containerization, orchestration platforms (Kubernetes, Docker), and cloud infrastructure (AWS, GCP).
• Strong analytical skills, excellent communication abilities, and a passion for solving complex performance problems in dynamic environments.

Strong candidates may also have experience with:

• Knowledge of modern profiling tools (e.g., perf, eBPF) and techniques for low-level performance measurement and debugging.

What You'll Achieve

• Create systems that empower teams to identify and address performance anomalies proactively, reducing downtime and resource waste.
• Leverage data-driven insights to drive system optimizations that balance performance, scalability, and cost efficiency.
• Contribute to a culture of continuous improvement, using innovative statistical and machine learning methods to shape the future of performance insights.

This role is ideal for an engineer who thrives in a fast-paced, data-driven environment and is eager to make a significant impact on the performance, reliability and efficiency of critical systems.