AI Performance Engineer

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.