ArcOne - Performance Engineer - AI Systems

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.
, 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.