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About
As a Machine Learning Engineer focused on model optimization algorithms, you will work closely with our product and research teams to develop state‑of‑the‑art deep learning software. You will collaborate with technical and research teams to develop LLM training and deployment pipelines, implement model compression algorithms, and productize deep learning research. This role is for those who enjoy bridging research and production, optimizing large models, and contributing to open‑source AI tooling.
Responsibilities
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.
Qualifications
Strong understanding of machine learning and deep learning fundamentals with experience in one or more of LLM inference optimizations and NLP.
Experience with tensor math libraries such as PyTorch and NumPy.
Strong programming skills with proven experience implementing Python‑based machine learning solutions.
Ability to develop and implement research ideas and algorithms.
Experience with mathematical software, especially linear algebra.
Understanding of linear algebra, gradients, probability, and graph theory.
Strong communication skills with both technical and non‑technical team members.
BS or MS in computer science, computer engineering, or a related field. A PhD in an ML‑related domain is considered a strong plus.
Benefits Comprehensive medical, dental, and vision coverage; Flexible Spending Account; Health Savings Account; Retirement 401(k) with employer match; Paid time off and holidays; Paid parental leave; Leave benefits including disability, paid family medical leave, and paid military leave; Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more. (Note: These benefits are only applicable to full‑time, permanent associates at Red Hat located in the United States.)
Inclusion at Red Hat Red Hat’s culture is built on the open‑source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village.
Equal Opportunity Policy (EEO) Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.
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Languages
- English
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