Hewlett Packard Labs - Scientific Machine Learning Research Engineer

Hewlett Packard Labs - Scientific Machine Learning Research Engineer

This role has been designed as ''Onsite' with an expectation that you will primarily work 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 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.

Job Description:

Role and Responsibilities

The Large-Scale Integrated Photonics Laboratory at Hewlett Packard Labs has an immediate opening for a research engineer with background in scientific machine learning (SciML).

A successful candidate must have demonstrated the ability to apply of contemporary optimization, machine learning, and artificial intelligence approaches to the simulation and design of scientific systems, circuits, and devices in a research setting.

This role requires effective teamwork and communication, working in an interdisciplinary research environment, and creative/technical problem solving at a high level.

Excellent written and oral communications skills are a requirement.

Qualifications and Education Requirements


* PhD in electrical engineering, computer science, or related areas.

Preferred Skills


* Proficiency in and deep understanding of machine learning and/or artificial intelligence.


* Experience in applying ML/AI methods in a scientific setting (prior academic publication on the topic is a strong plus).


* Proficiency in statistical (Bayesian) learning.


* Broad knowledge of and experience with optimization techniques and algorithms.


* Familiarity with analog computing and its current challenges (familiarity with optical computing specifically is a plus).


* Familiarity with conventional numerical PDE simulation methods, such as finite-element and finite-difference methods (common electromagnetic simulation methods are a plus).


* Proficiency in PyTorch, JAX, TensorFlow, and/or other common machine learning software tools.


* Proficiency/familiarity with interacting with and utilizing shared high-performance computing resources (e.g., use of MPI and GPUs on shared resources).


* Proficiency with Linux OS, Python virtual environments and package management, Git version control, and other common software development workflows.

Additional Skills:

Accountability, Accountability, Action Planning, Active Learning (Inactive), Active Listening, Agile Methodology, Agile Scrum Development, Analytical Thi...