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Staff Machine Learning Engineer - Santa Clara California
Company: Nutanix Location: Santa Clara, California
Posted On: 05/07/2024
About the TeamThe AI / Machine Learning team is building the industry's leading Machine Learning models for use cases at scale. This team is also responsible for pushing the boundaries of applied Machine Learning and state-of-the-art Generative AI techniques on a challenging and diverse dataset. The team is at the forefront of building frameworks for Responsible AI development.Primary Responsibilities: - In this senior-level role, you will own, train, build and deploy cutting edge deep learning models across all Eightfold products, end to end.
- Build on top of Open Source LLM (Large Language Models) to leverage a diverse dataset. -
- Apply innovative solutions from Generative AI
- Create industry best practices for Machine Learning for Recruiting and HR industry around the globe
- Do it responsibly to provide equal opportunity for everyone by extending our internal model fairness platform
- Create innovative algorithms for Machine Learning & AI
- Implement best practices for building AI-enabled products
- Develop AI-based systems for Natural Language Processing (NLP)
- Optimize Machine Learning models for time efficiency, performance, cost, scalability, and accuracy
- Develop tools and processes for automatically train, updating and evaluate LLM (Large Language Models)Qualifications:
- Strong foundation in Machine Learning (ML), Deep Learning, LLMs and NLP
- Hands-on experience in applying Natural Language Processing solutions to challenging real-world problems.
- Ability to work cross-functionally & interface with data science experts across all Eightfol's customer base
- Familiar with LLM (Large Language Models), transformers like BERT, GPTs, T-5, HuggingFace etc.
- Exceptionally strong knowledge of CS fundamental concepts and ML languages ( like Python, C, C++, Java, JavaScript, R, and Scala, etc. )
- Ability to innovate, as proven by a track record of software artifacts or academic publications in applied machine learning.
- Prior experience building and deploying machine learning models in production at scale
- Understanding of data and ML systems with the ability to think across stack layers - REST APIs, microservices, data ingestion and processing systems, and distributed systems.
- Extensive experience with scientific libraries in Python (numba, pandas) and machine learning tools and frameworks (scikit-learn, tensorflow, torch, etc.).
- Experience implementing production machine learning systems, working with large-scale datasets, and a solid understanding of machine learning theory.
- Familiar with a cloud-based environment such as AWS, Azure or GCPPreferred Qualifications:
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