MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Machine Learning Engineer at Red Hat focused on AI model optimization and collaboration with research teams. Developing software for LLM training and deployment in enterprise settings.
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.
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.
- A PhD in a ML related domain is considered a strong plus.
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Professional development opportunities
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