Usama Baloch is a Machine Learning Engineer with five years’ experience building and optimizing ML systems, currently contributing to Unify’s Ivy project to unify ML frameworks. He develops backend features and implements core operations and TensorRT support for Ivy, benchmarks HuggingFace models across targets, and applies sparsification techniques like quantization and pruning to improve performance. Usama also fine-tunes LLMs with LoRA/PEFT and vLLM for domain tasks and has worked on GC and autotuner components, showing a blend of low-level engine work and applied model optimization. Based in Karachi, he pairs academic grounding from the National University of Computer and Emerging Sciences with hands-on open-source contributions—adding PyTorch front-end functions and backend logic for JAX and TensorFlow—to help make framework interoperability practical. Notably, his contributions touch both framework internals and real-world deployment tooling, a combination that accelerates moving SOTA models from research to production.
5 years of coding experience
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at National University of Computer and Emerging Sciences
Contributions:24 reviews, 4 commits, 29 PRs in 1 month
Contributions summary:Usama contributed to the ivy project by implementing and testing new functions within the PyTorch frontend. These changes included adding instance methods like `cosh_`, `acosh_`, and `atan2_`, and functional methods like `real` and `fmod`. The user's work involved modifying files in the `ivy/functional/frontends/torch` and `ivy_tests/test_ivy/test_frontends/test_torch` directories. Furthermore, the user also made improvements for the jax and tensorflow backends by adding related logic functions.
Contributions:2 pushes, 1 branch in 3 years 1 month
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