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 optimizing AI models and contributing to open-source AI tooling. Collaborating on model compression algorithms and deploying deep learning research solutions.
Responsibilities
- Contribute to the design, development, and testing of various inference optimization algorithms
- Design, implement, and optimize model compression pipelines
- Develop and maintain speculative decoding frameworks
- Collaborate closely with research scientists to translate experimental ideas into robust, production-ready systems
- Profile and optimize end-to-end LLM performance
- 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
- Contribute to open-source projects, code reviews, and documentation
- Mentor and guide team members, fostering a culture of continuous learning and innovation
- Stay current with LLM architectures, inference optimizations, and hardware advancements.
Requirements
- Strong understanding of machine learning and deep learning fundamentals
- Experience in 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
- Flexible working hours
- Professional development opportunities
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