Hewlett Packard Labs - AI Data Foundation Post-doc Research Engineer

Key Skills

Java Programming, Analytical and Problem solving, Management, Python Programming, C++, C Programming, Framework, SQL, Big Data Technology, Apache Kafka, Machine learning techniques, Deep Learning, PyTorch, Kubernetes-K8s, CUDA/GPU programming, Algorithms, SPARK Programming, DataOps, MLOps tools, Data pipelines, Common data structures

Job Description

Innovation drives progress in today’s world and Hewlett Packard Labs is charged with solving some of the most complex challenges for Hewlett Packard Enterprise businesses with a relentless focus on delivering technology breakthroughs that can transform the world and dramatically improve tomorrow’s products and solutions. Labs works hand-in-hand with HPE’s businesses, bringing our innovation roadmaps together with our business roadmaps, integrating key technologies into our products, and accelerating how we drive technology from R&D to commercialization.

The Artificial Intelligence Research Lab is a team at Hewlett Packard Labs that focuses on researching and developing a range of technologies, architectures, software and systems advancements in key areas of Deep Learning and Machine Learning with an emphasis on cutting edge challenges in Trustworthy AI, Data centric & Edge AI and AI for Science. This is a unique opportunity to work at a start-up pace within a big company. We are looking for a deep hands on entrepreneurial mind-set, and a passion to take a problem and iteratively develop elegant well-engineered solutions with a fast turnaround time. You will have an opportunity to work with Machine Learning experts in different areas including software engineering, hardware optimizations, data pipelining, and techniques from latest AI research.

Role and Responsibilities

We are seeking candidates interested in research and development of advanced technologies in Data-centric AI, including search, enhancement and interpretation of meta-data and lineage for AI datasets that would enable discovery, selection and usage of relevant high quality data for trustworthy AI outcomes across multiple AI applications. We are particularly interested in individuals with a background in computer systems, big data pipelines, data management, distributed storage systems, machine learning, deep learning, and statistics, with a good understanding of the current state of the art, major trends and opportunities, and a demonstrated track record in innovative research. The ideal candidate can thrive in an applied research environment, balancing significant technical contributions published externally in open source with the hands-on engineering skill to bring such contributions to practice in partnering with our internal software development teams and external partners.

Qualifications and Education Requirements

PhD in Computer Science or related fields with a focus on data management, big data, or Machine Learning

Preferred Skills

• Expertise in research of complex data layer technologies (data management systems, storage systems, filesystems)
• Familiarity with AI, Machine Learning and Deep Learning algorithms
• Experience in big data/ML/deep learning frameworks (e.g. Spark, Kafka, Tensorflow/Pytorch), end-to-end pipelines for AI and Machine Learning and in particular the data layer underlying the pipelines.
• Familiarity with tools and frameworks for DataOps, MLOps and Trustworthy AI
• Strong programming skills in Python, C/C++, with high proficiency in data structures and algorithms
• Outstanding analytical and problem solving skills.
• Experience in system software performance and scalability optimization – a plus
• Experience with multi-threaded programming, parallel processing, OOD/OOP/distributed programming – a plus
• Experience in containerized development and orchestration tools (e.g. Kubernetes, Ezmeral) – a plus
• Experience in deep learning research, GPU acceleration, and Model Optimization – a plus