Senior ML Research Engineer

Job Summary

Are you passionate about advancing the frontiers of efficient AI? Do you enjoy turning theoretical insights into practical tools that power large-scale machine learning systems? Are you excited by the challenge of scaling, compressing, and accelerating state-of-the-art models?

We are looking for a Senior Machine Learning Research Engineer with a strong research background and hands‑on experience in building and optimizing deep learning models. In this role, you will explore and develop cutting‑edge techniques in model compression and post‑training optimization, including pruning, quantization, knowledge distillation, and speculative decoding. You will help design and evaluate novel algorithms that bridge theory and real‑world deployment.

This is a unique opportunity to work at the intersection of applied research and high‑performance machine learning systems, where your contributions can lead to publications, open‑source tools, and deployment in production environments.

• Design and conduct experiments to evaluate model compression and post‑training optimization strategies for large‑scale deep learning models.
• Develop scalable and modular research code in Python.
• Work closely with software engineers and product teams to translate research into deployable systems.
• Explore emerging techniques in efficient inference and help define future directions for model optimization.
• Collaborate on publications in top‑tier ML/AI conferences and contribute to open‑source initiatives.
• Benchmark models across hardware configurations, contributing to the broader understanding of how model optimizations affect performance in real‑world deployment scenarios.
• Participate in reading groups, internal workshops, and mentoring activities.

• PhD in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
• Strong foundation in machine learning algorithms and numerical optimization.
• Proficiency in Python and deep learning frameworks such as PyTorch, Tensor Flow, or JAX.
• Strong analytical and problem‑solving skills.
• Experience with experimental design and empirical research, including model evaluation and benchmarking.
• Excellent written and verbal communication skills, including the ability to explain complex ideas to a technical audience.

• Familiarity with model compression techniques such as quantization, pruning, knowledge distillation, or speculative decoding.
• Experience contributing to open‑source machine learning projects.
• Experience optimizing model performance for inference efficiency, particularly on GPUs or specialized accelerators.
• Publication record in top‑tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR).
• Comfortable navigating large codebases and collaborating in a research‑oriented engineering team.

• A dynamic and intellectually stimulating environment with opportunities to shape the future of efficient ML systems.
• A collaborative team that values curiosity, creativity, and impact.
• Support for academic engagement (publishing, conference travel, workshops).
• Access to high‑performance computing resources and state‑of‑the‑art ML infrastructure.
• Comprehensive benefits, flexible work arrangements, and opportunities for career growth.

The salary range for this position is $ - $. Actual offer will be based on your qualifications.

Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat’s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience.

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