As a Machine Learning Engineer focused on model optimization algorithms, you will work closely with our product and research teams to develop SOTA deep learning software. You will collaborate with our technical and research teams to develop LLM training and deployment pipelines, implement model compression algorithms, and productize deep learning research. If you are someone who enjoys bridging research and production, optimizing large models, and contributing to open-source AI tooling, this role is for you.

Contribute to the design, development, and testing of various inference optimization algorithms in the LLM-compressor, Speculators, and vLLM projects.

Design, implement, and optimize model compression pipelines using techniques such as quantization and pruning.

Develop and maintain speculative decoding frameworks to improve inference speed while maintaining model accuracy.

Collaborate closely with research scientists to translate experimental ideas into robust, production-ready systems

Profile and optimize end-to-end LLM performance, including memory usage, latency, and throughput

Benchmark, evaluate, and implement strategies for optimal performance on target hardware

Build tools to streamline model training, evaluation, and deployment.

Participate in technical design discussions and propose innovative solutions to complex problems

Contribute to open-source projects, code reviews, and documentation; collaborate with internal and external contributors.

Mentor and guide team members, fostering a culture of continuous learning and innovation.

Stay current with LLM architectures, inference optimizations, quantization research, and CPU/GPU hardware advancements.