Hewlett Packard Enterprise
Hewlett Packard Labs - Senior AI/ML Research Scientist
Hewlett Packard Enterprise, Milpitas, California, United States, 95035
Hewlett Packard Labs -- Senior AI/ML Research Scientist
This role has been designed as ‘Hybrid’ with an expectation that you will work on average 2 days per week from an HPE office.
Overview
Who We Are:
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know diverse backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. While generative AI models, such as Large Language Models (LLMs) and diffusion models, have reached unprecedented results, they are inefficient and lack interpretability, especially when involved in complex reasoning and scientific computing. In the Emergent Machine Intelligence Team at Hewlett Packard Labs, we are dedicated to pushing the boundaries of what’s possible with artificial intelligence. As a Senior Research Scientist, you will be at the forefront of our efforts to develop groundbreaking generative AI models. You will be crafting cutting-edge algorithms and applications by augmenting state-of-the-art LLMs with extensive test-time compute, adopting tools from symbolic AI, incorporating physics simulators, and employing insight from statistical physics and non-equilibrium thermodynamics. You will work closely with a multidisciplinary team of engineers, researchers, and product managers, during full development cycles of pre-training, fine-tuning, and inference. Together, our goal is to build scalable, efficient, and innovative generative AI systems. Join us to be a part of a team that shapes the future of high-performance AI. Key Responsibilities
Conduct high-quality research in generative AI, including but not limited to designing algorithms for pre-training and post-training autoregressive and diffusion models for multimodal data. Design, implement, and validate new algorithms and models for augmented LLMs, pushing the boundaries of AI capabilities. Develop novel algorithms for fine-tuning, retrieval augmented generation, and in-context learning for various generative models. Develop algorithms for training and inference in Energy-Based Models. Collaborate with cross-functional teams to apply research findings to develop new products or enhance existing ones. Publish research papers in top-tier journals and conferences, sharing findings with the broader scientific community. Stay abreast of the latest AI research and trends, identifying opportunities for innovation and improvement. Mentor junior researchers and engineers, fostering a culture of knowledge sharing and collaboration. Develop prototypes and proof-of-concept implementations to demonstrate the potential of research findings. Engage with the academic community by attending conferences, workshops, and seminars. Required skills and experience
PhD in Computer Science, Artificial Intelligence, Machine Learning, Physics, Mathematics, or related fields. 5+ years of experience with training and fine-tuning generative AI models including LLMs, diffusion models, or Energy-Based Models Experience with test-time compute techniques, such as chain-of-thoughts, self-consistency, or reinforcement learning based verifiers Proven track record of research in generative models, demonstrated through first-tier publications (e.g., NeurIPS, ICML, ICLR, or high-impact journals), patents, or publicly available projects. Proficiency in programming languages used in AI research (e.g., Python) and experience with AI/ML frameworks (e.g., TensorFlow, PyTorch). Deep understanding of machine learning algorithms and principles in the context of generative AI. Strong mathematical background with skills in statistics, probability, and linear algebra. Creative and analytical thinking abilities with a passion for solving complex problems. Excellent communication skills, able to convey complex ideas clearly to technical and non-technical audiences. Desired skills and experience
Knowledge of computational physics, statistical physics, many-body physics simulations, or quantum computing, and how to apply these principles to develop innovative AI algorithms and models. Proficiency with hosting and operating locally generative AI models through frameworks such as ollama, vllm, etc. Background in discrete optimization, combinatorial optimization, Monte Carlo methods, quantum-inspired algorithms, and applying these disciplines to enhance AI models. Programming experience in high-performance computing (HPC) environments. Experience with cloud computing platforms, GPU computing, or FPGA computing. Additional Skills
Artificial Intelligence Technologies, Cross Domain Knowledge, Data Engineering, Data Science, Design Thinking, Development Fundamentals, Full Stack Development, IT Performance, Machine Learning Operations, Scalability Testing, Security-First Mindset What We Can Offer You
Health & Wellbeing: We strive to provide a comprehensive suite of benefits that supports wellbeing. Personal & Professional Development: We invest in your career with programs to help you reach career goals. Unconditional Inclusion: We are unconditionally inclusive and value varied backgrounds; we manage work and personal needs. Let’s Stay Connected: Follow HPECareers on social channels to see the latest on people, culture and tech at HPE. Job: Engineering Job Level: TCP_04 Salary and Benefits
The expected salary range for a U.S.-based hire is USD 133,500.00 - 307,000.00 per year. Actual offers may vary by location, experience, and qualifications. Information about employee benefits is available at the official rewards site. HPE is an Equal Employment Opportunity employer. We do not discriminate on race, gender, or any other protected category. Our goal is to be one global team that is representative of our customers in an inclusive environment where we can innovate and grow together. Equal Employment Opportunity Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities. HPE will comply with laws related to employer use of arrest and conviction records.
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Who We Are:
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know diverse backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. While generative AI models, such as Large Language Models (LLMs) and diffusion models, have reached unprecedented results, they are inefficient and lack interpretability, especially when involved in complex reasoning and scientific computing. In the Emergent Machine Intelligence Team at Hewlett Packard Labs, we are dedicated to pushing the boundaries of what’s possible with artificial intelligence. As a Senior Research Scientist, you will be at the forefront of our efforts to develop groundbreaking generative AI models. You will be crafting cutting-edge algorithms and applications by augmenting state-of-the-art LLMs with extensive test-time compute, adopting tools from symbolic AI, incorporating physics simulators, and employing insight from statistical physics and non-equilibrium thermodynamics. You will work closely with a multidisciplinary team of engineers, researchers, and product managers, during full development cycles of pre-training, fine-tuning, and inference. Together, our goal is to build scalable, efficient, and innovative generative AI systems. Join us to be a part of a team that shapes the future of high-performance AI. Key Responsibilities
Conduct high-quality research in generative AI, including but not limited to designing algorithms for pre-training and post-training autoregressive and diffusion models for multimodal data. Design, implement, and validate new algorithms and models for augmented LLMs, pushing the boundaries of AI capabilities. Develop novel algorithms for fine-tuning, retrieval augmented generation, and in-context learning for various generative models. Develop algorithms for training and inference in Energy-Based Models. Collaborate with cross-functional teams to apply research findings to develop new products or enhance existing ones. Publish research papers in top-tier journals and conferences, sharing findings with the broader scientific community. Stay abreast of the latest AI research and trends, identifying opportunities for innovation and improvement. Mentor junior researchers and engineers, fostering a culture of knowledge sharing and collaboration. Develop prototypes and proof-of-concept implementations to demonstrate the potential of research findings. Engage with the academic community by attending conferences, workshops, and seminars. Required skills and experience
PhD in Computer Science, Artificial Intelligence, Machine Learning, Physics, Mathematics, or related fields. 5+ years of experience with training and fine-tuning generative AI models including LLMs, diffusion models, or Energy-Based Models Experience with test-time compute techniques, such as chain-of-thoughts, self-consistency, or reinforcement learning based verifiers Proven track record of research in generative models, demonstrated through first-tier publications (e.g., NeurIPS, ICML, ICLR, or high-impact journals), patents, or publicly available projects. Proficiency in programming languages used in AI research (e.g., Python) and experience with AI/ML frameworks (e.g., TensorFlow, PyTorch). Deep understanding of machine learning algorithms and principles in the context of generative AI. Strong mathematical background with skills in statistics, probability, and linear algebra. Creative and analytical thinking abilities with a passion for solving complex problems. Excellent communication skills, able to convey complex ideas clearly to technical and non-technical audiences. Desired skills and experience
Knowledge of computational physics, statistical physics, many-body physics simulations, or quantum computing, and how to apply these principles to develop innovative AI algorithms and models. Proficiency with hosting and operating locally generative AI models through frameworks such as ollama, vllm, etc. Background in discrete optimization, combinatorial optimization, Monte Carlo methods, quantum-inspired algorithms, and applying these disciplines to enhance AI models. Programming experience in high-performance computing (HPC) environments. Experience with cloud computing platforms, GPU computing, or FPGA computing. Additional Skills
Artificial Intelligence Technologies, Cross Domain Knowledge, Data Engineering, Data Science, Design Thinking, Development Fundamentals, Full Stack Development, IT Performance, Machine Learning Operations, Scalability Testing, Security-First Mindset What We Can Offer You
Health & Wellbeing: We strive to provide a comprehensive suite of benefits that supports wellbeing. Personal & Professional Development: We invest in your career with programs to help you reach career goals. Unconditional Inclusion: We are unconditionally inclusive and value varied backgrounds; we manage work and personal needs. Let’s Stay Connected: Follow HPECareers on social channels to see the latest on people, culture and tech at HPE. Job: Engineering Job Level: TCP_04 Salary and Benefits
The expected salary range for a U.S.-based hire is USD 133,500.00 - 307,000.00 per year. Actual offers may vary by location, experience, and qualifications. Information about employee benefits is available at the official rewards site. HPE is an Equal Employment Opportunity employer. We do not discriminate on race, gender, or any other protected category. Our goal is to be one global team that is representative of our customers in an inclusive environment where we can innovate and grow together. Equal Employment Opportunity Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities. HPE will comply with laws related to employer use of arrest and conviction records.
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