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Hewlett Packard Enterprise

Builds servers, networking and private cloud

AI and Machine Learning Engineer 3

AI ResearchPosted on Nov 10
Full-timeMilpitas, CA, USA

About the Job

Requirements

  • A PhD completed or expected soon in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or a related field, with a research focus in machine learning.
  • Typically 4 years of experience, including graduate and doctoral research.
  • A strong foundation in deep learning, optimization, probabilistic modeling, reinforcement learning, or related machine-learning methods, demonstrated through doctoral research, internships, or publications.
  • Ability to implement and evaluate research ideas in Python using frameworks such as PyTorch, with sound experimental and software-engineering practices.
  • Experience applying experimental design, benchmarking, ablation studies, and statistical analysis to collect, validate, and analyze research data.
  • Ability to communicate research plans, proposals, and results, and negotiate options at management levels.

Responsibilities

  • Participate as a member of the Hewlett Packard Labs Machine Learning research team, carrying out the investigation, design, development, execution, and implementation of AI tokenomics research to generate new products, technologies, and intellectual property.
  • Research and prototype methods that learn representations of jobs and system states, and predict workload complexity, answer quality, latency, token demand, energy, and cost.
  • Develop routing policies, model cascades, and adaptive controllers that select the appropriate LLM, reasoning mode, retrieval strategy, tool chain, and deployment target (local, edge, private cloud, or public cloud) for each job.
  • Develop methods to assign and enforce token, context, reasoning, and compute budgets based on job difficulty, priority, risk, service-level objectives, and expected business value.
  • Build benchmark, telemetry, accounting, and workload-simulation environments.
  • Conduct controlled experiments, ablation studies, sensitivity analyses, and stress tests across model families, hardware platforms, and deployment environments. Evaluate answer quality, routing accuracy, token efficiency, tail latency, throughput, reliability, cost per completed job, energy per token, and carbon per completed job, and communicate results to team members for review and feedback.
  • Collaborate with internal and external researchers and engineers to validate results, and help transfer promising technologies into HPE AI platforms.
  • Prepare papers and presentations for peer review, publication, and delivery at leading ML conferences and industry events. Create patent disclosures and supporting documentation.

Desired Qualifications

  • Research experience with machine learning, LLMs, model routing, reinforcement learning, causal inference, constrained optimization, retrieval-augmented generation, or agentic AI.
  • Interest or experience in token-efficient inference, per-job compute budgeting, multi-model serving, or edge-to-cloud AI systems.
  • Interest or experience in sustainable AI, energy-aware computing, or carbon-aware scheduling and optimization.

About the Company

Hewlett Packard Enterprise

Builds servers, networking and private cloud

Hewlett Packard Enterprise sells servers, storage, networking and hybrid cloud software to large companies, governments and service providers. Its GreenLake platform lets customers run on-premises hardware as a metered cloud service, and its Cray supercomputers run national lab and AI training workloads. The networking business, built on Aruba and Juniper Networks, supplies campus, data-center and routing equipment. Revenue comes from hardware sales, multiyear support contracts, financing and subscriptions. The company traces its roots to Hewlett-Packard, founded in 1939, became a separate public company in 2015 and is headquartered in Houston, Texas.

Company size
10,001+
Company stage
IPO
Headquarters
Houston, TX
Founded
1939

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