Contribute to the design, development, and testing of various inference optimization algorithms in the vLLM , and related projects, such as llm-d , LLM-compressor and speculators .
Create and manage inference serving deployment pipelines
Benchmark, profile, and evaluate different parallelizations, quantization and sparsification approaches to determine the best performance for specific hardware and models
Participate in technical design discussions and provide innovative solutions to complex problems
Stay up-to-date with the latest advancements in the open source LLM model architecture, LLM Inference parallelizations/optimizations techniques, and quantization research
Stay up-to-date of latest CPU and GPU hardware architecture and features to boost AI inference performance
Give thoughtful and prompt code reviews
Mentor and guide other engineers and foster a culture of continuous learning and innovation
Continuous collaboration with internal and external open source comitters and contributors while contributing to vLLM and related projects
Requirements
Strong understanding of machine learning and deep learning fundamentals with experience in one or more of LLM Inference Optimizations, Computer Vision, NLP, and reinforcement learning
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. A PhD in a ML related domain is considered a strong plus.
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The salary range for this position is $206,600.00 - $351,050.00. Actual offer will be based on your qualifications.
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About Red Hat
Benefits
Comprehensive medical, dental, and vision coverageFlexible Spending Account - healthcare and dependent careHealth Savings Account - high deductible medical planRetirement 401(k) with employer matchPaid time off and holidaysPaid parental leave plans for all new parentsLeave benefits including disability, paid family medical leave, and paid military leaveAdditional 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 UnitHealth insuranceDental insuranceVision insurance401(k)Remote work optionsFlexible scheduleEquity / stock optionsPerformance bonusParental leave
Additional Information
Job Summary
At Red Hat we believe the future of AI is open and we are on a mission to bring the power of open-source LLMs and vLLM to every enterprise. Red Hat AI Inference team accelerates AI for the enterprise and brings operational simplicity to GenAI deployments. As leading developers, maintainers of the vLLM project, and inventors of state-of-the-art techniques for model quantization and sparsification, our team provides a stable platform for enterprises to build, optimize, and scale LLM deployments.
As a Senior Principal 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 wants to contribute to solving challenging technical problems at the forefront of deep learning in the open source way, this is the role for you.
Join us in shaping the future of AI!
Red Hat · Boston