AI and Machine Learning Engineer III

Hewlett Packard Enterprise • Milpitas, CA

1d • $143,000 - $290,500 • Hybrid

About The Position

The AI and Machine Learning Engineer III joins Hewlett Packard Labs’ Machine Learning research team to investigate, design, develop, execute, and implement AI tokenomics research that can generate products, technologies, and intellectual property. The role applies machine-learning expertise to technical problems, recommends alternatives, and may lead research prototypes while assisting junior professionals and interns. It exercises independent judgment and works with researchers, engineers, and platform partners to determine effective approaches and meet objectives.

The position researches and prototypes methods for representing jobs and system states and predicting workload complexity, answer quality, latency, token demand, energy use, and cost. It develops routing policies, model cascades, adaptive controllers, and budgeting methods for selecting models, reasoning modes, retrieval strategies, tools, deployment targets, and compute resources. The role also builds benchmarking, telemetry, accounting, and workload-simulation environments; conducts controlled evaluations across models, hardware, and deployment environments; and communicates findings for review.

The engineer collaborates with internal and external researchers and engineers to validate results and help transfer promising technologies into HPE AI platforms. The role prepares papers and presentations for peer review, publication, conferences, and industry events, and creates patent disclosures and supporting documentation. This role is hybrid, requiring an average of two days per week in an HPE office.

Requirements

• 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 four years of experience, including graduate and doctoral research.
• 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.
• Ability to apply experimental design, benchmarking, ablation studies, and statistical analysis to collect, validate, and analyze research data.
• Strong analytical and problem-solving skills.
• Excellent written and verbal communication skills.
• Ability to communicate research plans, proposals, and results effectively and negotiate options at management levels.

Nice To Haves

• Research experience with ML, 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.

Responsibilities

• Participate in the Hewlett Packard Labs Machine Learning research team and carry out AI tokenomics research to generate products, technologies, and intellectual property.
• Research and prototype methods that represent jobs and system states and predict workload complexity, answer quality, latency, token demand, energy, and cost.
• Develop routing policies, model cascades, and adaptive controllers to select LLMs, reasoning modes, retrieval strategies, tool chains, and deployment targets.
• 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; communicate results 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 ML conferences and industry events.
• Create patent disclosures and supporting documentation.
• Potentially lead research prototypes and provide assistance to lower-level professionals and interns.

Benefits

• Comprehensive benefits supporting physical, financial, and emotional wellbeing.
• Career development programs to help employees reach career goals or apply their skills in another division.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree