Principal Machine Learning Engineer
About The Position
At Red Hat, we are dedicated to advancing the future of AI through open-source solutions, specifically focusing on bringing the power of open-source LLMs and vLLM to enterprises. Our AI Inference team is at the forefront of accelerating AI for businesses and simplifying GenAI deployments. As key contributors to the vLLM project and pioneers in model quantization and sparsification techniques, we provide a robust platform for enterprises to build, optimize, and scale their LLM deployments. This role is for a Machine Learning Engineer specializing in model optimization algorithms, who will collaborate with our product and research teams to develop state-of-the-art deep learning software. You will be instrumental in developing LLM training and deployment pipelines, implementing model compression algorithms, and bringing deep learning research into production. If you thrive on bridging the gap between research and production, optimizing large models, and contributing to open-source AI tooling, this is the opportunity for you. Join us in shaping the future of AI!
Requirements
- 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 communications skills with both technical and non-technical team members
- BS, or MS in computer science or computer engineering or a related field.
- Must have the ability to work without a need for current or future visa sponsorship.
Nice To Haves
- A PhD in a ML related domain is considered a strong plus.
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.
Benefits
- Comprehensive medical, dental, and vision coverage
- Flexible Spending Account - healthcare and dependent care
- Health Savings Account - high deductible medical plan
- Retirement 401(k) with employer match
- Paid time off and holidays
- Paid parental leave plans for all new parents
- 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!
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What This Job Offers
Job Type
Full-time
Career Level
Principal
