Performance Engineer

A Performance Engineer at Cerebras Systems is tasked with developing models for our innovative hardware and software stack while estimating end-to-end performance. The responsibilities also include analyzing performance, identifying bottlenecks, and discovering optimization opportunities. Collaborating closely with design teams, you will optimize microcode kernels and algorithms, ensuring that the architecture is utilized to its fullest potential.

To qualify for the Performance Engineer role at Cerebras Systems, candidates should have a degree in Computer Science, Electrical Engineering, or a related field focusing on computer architecture. Experience with performance analysis on CPUs, GPUs, and parallel architectures, along with programming skills in C/C++ and Python, are essential. Familiarity with Machine Learning models and data flow is also beneficial.

At Cerebras Systems, a Performance Engineer works on various projects centered around optimizing hardware and software performance for our AI chip. This involves developing tools for performance analysis, collaborative optimization of algorithms, and implementing solutions that facilitate large-scale machine learning applications. Projects often entail working on solutions that are at the cutting edge of AI technology.

The work culture at Cerebras Systems for Performance Engineers is dynamic and inclusive. Employees appreciate the startup vitality combined with job stability, fostering an environment that encourages innovation and individual contributions. Team members value the non-corporate atmosphere that respects diverse beliefs and focuses on continuous learning and support.

The role of a Performance Engineer at Cerebras Systems plays a crucial part in advancing AI technology. By focusing on end-to-end performance optimization, this position directly influences the efficiency and effectiveness of AI applications on our groundbreaking hardware. The improvements made by Performance Engineers enable users to run large-scale ML applications seamlessly, transforming industries that utilize AI.

To prioritize performance bottlenecks, I typically start by identifying the most resource-intensive processes through profiling tools. By analyzing metrics such as CPU and GPU utilization, I can determine where the largest gains can be made. I also consider the impact on overall system performance and user experience, ensuring that high-impact areas are addressed first.

In my previous roles, I've conducted extensive performance analysis on CPUs, GPUs, and parallel architectures, utilizing profiling tools to gather vital performance data. I have worked on optimizing algorithms tailored to each architecture, focusing on efficiency and speed. Adjusting the code to leverage architecture-specific features has allowed me to achieve significant improvements in performance.

To optimize microcode kernels, I analyze the kernel's execution flow and assess its interaction with hardware. Techniques include loop unrolling, instruction scheduling, and memory access optimization to reduce latency. I also ensure that tiles are optimally sized for the architecture, allowing for maximum parallel processing and improved throughput.

I have a solid understanding of various Machine Learning models and their underlying algorithms. This knowledge is valuable in my role as a Performance Engineer because it enables me to tailor optimizations to specific types of workloads effectively. By leveraging my understanding of data flow, I can implement more efficient processing solutions for ML applications, yielding better performance outcomes.

Collaboration involves regular communication and a shared understanding of goals between hardware and software design teams. I promote a team-oriented approach by scheduling joint review sessions where we discuss performance metrics and optimization strategies. This alignment ensures that both hardware constraints and software requirements are taken into account, leading to effective and efficient performance improvements.

I primarily use C/C++ and Python for performance analysis tasks. C/C++ provides the low-level control necessary for intricate performance tuning, while Python allows for rapid prototyping and the easy integration of monitoring tools. This combination gives me the ability to analyze low-level performance while also developing high-level scripts for automation and reporting.

I regularly participate in workshops and webinars, subscribe to relevant tech journals, and engage with online communities dedicated to performance engineering. Following industry leaders and key publications helps me stay informed on the latest techniques and technologies, ensuring that my skills and knowledge remain current.

When faced with unexpected performance regressions, I first conduct a thorough investigation using profiling tools to identify the root cause, comparing current performance to previous benchmarks. This process helps isolate the issue, and I collaborate with team members to brainstorm potential fixes or optimizations before re-testing to validate the changes.

In a previous role, I led a project where we optimized a critically used algorithm by over 30% through targeted analysis of its data processing architecture. By identifying redundant calculations and streamlining input/output operations, I was able to significantly enhance the performance outcome, thereby improving user experience and satisfaction.

I am driven by the challenge of continuously improving systems and processes. The opportunity to work on groundbreaking technologies, as we do at Cerebras Systems, is particularly inspiring. Contributing to significant advancements in AI and witnessing the real-world impact of my optimizations fuels my passion for performance engineering.