Uber Software Engineer Performance Insights 

United States, West Virginia 

629731289

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.

* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .