AI and Machine Learning Engineer III

AI and Machine Learning Engineer III

This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office.

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:

Management Level Definition:

Contributions include applying developed machine-learning subject matter expertise to solve common and sometimes complex technical problems, and recommending alternatives where necessary.

An example is balancing answer quality, latency, cost, energy, and carbon in hybrid LLM systems.

Might act as project lead for research prototypes and provide assistance to lower-level professionals and interns.

Exercises independent judgment and consults with researchers, engineers, and platform partners to determine the best method for accomplishing work and achieving objectives.

Responsibilities:


* Participates 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.


* Researches and prototypes methods that learn representations of jobs and system states, and that predict workload complexity, answer quality, latency, token demand, energy, and cost.


* Develops 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.


* Develops methods to assign and enforce token, context, reasoning, and compute budgets based on job difficulty, priority, risk, service-level objectives, and expected business value.


* Builds benchmark, telemetry, accounting, and workload-simulation environments.


* Conducts controlled experiments, ablation studies, sensitivity analyses, and stress tests across model families, hardware platforms, and deployment environments.

Evaluations measure answer quality, routing accuracy, token efficiency, tail latency, throughput, reliability, cost per completed job, energy per token, and carbon per completed job.

Communicates results to team members for review and feedback.


* Collaborates with internal and external researchers and engineers to validate results, and helps...