Position Overview: The Performance Engineer will play a critical role in analyzing, optimizing, and scaling ArcOne’s data and AI systems, with a focus on revenue management. This role involves deep performance profiling across application, middleware, runtime, and infrastructure layers, developing advanced observability tools, and collaborating with cross-functional teams to meet stringent latency, throughput, and scalability goals. Qualifications: Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Experience: 7+ years of software engineering experience, with a strong focus on performance or reliability engineering for high-scale distributed systems. Proven expertise in optimizing performance across one or more layers of the stack (e.g., database, networking, storage, application runtime, GC tuning, Python/Golang internals, GPU utilization). Hands-on experience with real-time and batch processing frameworks (e.g., Apache Kafka, Spark, Flink). Demonstrated success in building observability, benchmarking, or performance-focused infrastructure at scale. Experience in revenue management systems or similar domains (e.g., pricing, forecasting) is a plus. Technical Skills: Deep proficiency with performance profiling tools (e.g., perf, eBPF, VTune) and tracing systems (e.g., Jaeger, Open Telemetry). Strong understanding of OS internals, including scheduling, memory management, and IO patterns. Expertise in programming languages such as Python, Go, or Java, with a focus on runtime optimization. Key Responsibilities: Performance Analysis & Optimization: Analyze and optimize performance across the full stack, including application, middleware, runtime (e.g., Python runtime, GPU utilization), and infrastructure layers (e.g., networking, storage). Perform deep performance profiling, tuning, and optimization for databases, data pipelines, AI model inference, and distributed systems. Optimize critical components such as garbage collection (GC), memory management, IO patterns, and scheduling to ensure high efficiency. Observability & Tooling: Develop and maintain tooling and metrics to provide deep observability into system performance, enabling proactive identification of bottlenecks and inefficiencies. Implement and enhance performance monitoring systems (e.g., tracing, logging, dashboards) to track latency, throughput, and resource utilization in real-time. Contribute to benchmarking frameworks and performance-focused infrastructure to support continuous improvement. Cross-Functional Collaboration: Partner with infrastructure, platform, training, and product teams to define and achieve key performance goals for revenue management systems. Influence architecture and design decisions to prioritize latency, throughput, and scalability in large-scale data and AI systems. Align stakeholders around performance objectives, navigating ambiguity to deliver measurable improvements. Performance Testing & SLAs: Lead the development and execution of performance testing strategies, including load, stress, and scalability tests, for real-time and batch processing workloads. Define and monitor Service Level Agreements (SLAs) and Service Level Objectives (SLOs) around latency, throughput, and system reliability. Drive investigations into high-impact performance regressions or scalability issues in production, ensuring rapid resolution and root cause analysis. System Design & Scalability: Collaborate on the design of robust data architectures and AI systems, ensuring scalability and performance for revenue management use cases. Optimize real-time streaming (e.g., Apache Kafka, Flink) and batch processing (e.g., Spark, Hadoop) workloads for high-scale environments. Advocate for simplicity and rigor in system design to address complex performance challenges.
