Nicolas Martalog

Developed high-performance automation tools in Python and optimized distributed ML infrastructure pipelines, reducing runtime by 45%. Contributed to scaling workloads across GPU clusters with a focus on numerics, profiling, and performance tuning. Conducted quantitative analysis of ML benchmark performance, modeling the impact of parameters to develop optimization strategies and achieve up to 45% runtime improvements. Implemented automated processes using optical character recognition to optimize hardware accelerators, achieving testing accuracy exceeding 97%.

Designed and optimized REST APIs with .NET and Python, improving system efficiency by 50% and delivering maintainable backend services for reliable data processing. Worked with the DevOps team to build automated system monitoring pipelines using Python scripting and Splunk, improving production observability and reducing incident response time.

Engineered performance-critical math nodes in C++ using linear algebra and algorithms, boosting animation workflows by 25% with a focus on seamless user interactions. Wrote comprehensive unit tests in Python to ensure functionality, longevity, and security of the codebase compiler. Analyzed feature space of a deep learning model and engineered new data inputs, resulting in a 34% improvement in ML infrastructure performance.