Job
Description
Job Description : As a Performance Benchmarking Engineer your responsibilities include assessing and optimizing the efficiency and effectiveness of our language models. You will be responsible for designing, executing, and analysing performance benchmarks to ensure that our models meet or exceed industry standards. Your work will contribute to the continuous improvement and fine-tuning of our models for optimal performance across various applications and environments. Key Responsibilities : Benchmark Design and Execution - Develop comprehensive performance benchmarks to evaluate the speed, scalability, and resource utilization of our large language models. Design and implement experiments to assess model performance under various conditions, including different hardware configurations and input datasets. Performance Analysis - Conduct in-depth analysis of benchmark results to identify bottlenecks, inefficiencies, and areas for improvement in model performance. Collaborate with the development and engineering teams to implement optimizations based on benchmark findings. Benchmark Automation - Implement automated benchmarking processes to enable regular and consistent performance evaluations across model iterations. Develop scripts and tools to streamline the benchmarking workflow and ensure reproducibility of results. Scalability Testing - Evaluate the scalability of language models by assessing their performance with increasing data sizes and processing demands. Identify and address scalability challenges to enhance the model's effectiveness in handling larger workloads. Infrastructure Optimization - Collaborate with infrastructure and DevOps teams to optimize hardware and software configurations for peak model performance. Provide recommendations for infrastructure enhancements based on benchmarking results. Benchmark Reporting - Generate clear and concise reports summarizing benchmark results, key findings, and recommended optimizations. Communicate benchmark outcomes to relevant stakeholders, including developers, product managers, and executives. Skills & Tools Performance Testing Tools - Proficiency in using benchmarking tools and frameworks such as Apache JMeter, TensorFlow Benchmarking Suite, or custom scripts for performance evaluation. Benchmarking Datasets Aware of various Benchmarking data sets available (like HumanEval, Hellaswag, BoolQ etc) based on problem types like conversational, Coding, test cases etc. Deep understanding of performance evaluation metrics like relevance, faithfulness, coherence etc with respect to LLM input and output and how they can be measured / quantified Programming Languages - Strong programming skills in languages like Python, Bash, or other scripting languages for automation and tool development. Data Analysis Tools - Experience with data analysis tools and libraries (e.g., Pandas, NumPy) to derive insights from benchmark results. Cloud Services - Familiarity with cloud platforms (AWS, Azure, Google Cloud) and experience in benchmarking models across different cloud configurations. Containerization - Knowledge of containerization technologies (e.g., Docker, Kubernetes) and their impact on model performance. Statistical Analysis - Understanding of statistical methods for analyzing benchmark results and drawing meaningful conclusions.
