HP LABS - Big Data Internship
Location: Palo Alto, CA
The Systems Research Lab at HP Labs is seeking graduate students for internships in Palo Alto, CA. In particular, the cluster resource management group is looking for students to work on projects related to (a) cluster scheduling for big data applications on emerging hardware platforms, (b) Quality of Service for streaming workloads (Storm) and scalable key-value graph stores (Accumulo), and (c) analysis of monitoring and tracing data in distributed systems. Students will have the opportunity to work on HP Labs's ambitious research project called "The Machine" (http://www.hpl.hp.com/research/systems-research/themachine/).
REQUIREMENTS
Candidates must be enrolled in a graduate program in Computer Science or a relevant field with the following skills.
- Strong background in distributed systems and resource management
- Strong software development skills
- Knowledge of operating systems, networking and modern hardware architectures (massively multi-core processors, non-volatile memory)
- Experience with modern distributed platforms and workloads such as YARN, Storm, Spark or Accumulo a plus
- Strong communication skills
HOW TO APPLY
If interested, please contact Vanish Talwar (vanish.talwar@hp.com) or Adit Madan (adit.madan@hp.com).
Students should also apply online at: http://h30631.www3.hp.com/silicon-valley/hp-labs-research/jobid6844892-hp-labs-research-associate-systems-research-jobs?ss=paid
(HP Labs - Research Associate, Systems Research-1369533)
The online application also covers additional areas of research including:
- Large-scale distributed systems
- Operating systems internals, design and development
- Storage systems, file systems, and/or database systems
- Use of new hardware technologies (e.g. multi-core processors, non-volatile memory, GPUs)
- “Big data analytics” computing (e.g., MapReduce, Hadoop, Cassandra, BigTable)
- Extreme scale Indexing and Search algorithms, graph processing
- Large scale machine learning algorithms, pattern recognition, and non-Bayesian networks
- Dynamic resource management and scheduling
- Management of performance and quality-of-service
- Reliability, availability, and fault-tolerance
- Computer architecture
- Internet services and/or cloud computing infrastructure or middleware